Imprinting Github
library(tidyr)
library(ggplot2)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(matrixStats)
Attaching package: ‘matrixStats’
The following object is masked from ‘package:dplyr’:
countlibrary(DESeq2)Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min
Attaching package: ‘S4Vectors’
The following objects are masked from ‘package:dplyr’:
first, rename
The following object is masked from ‘package:tidyr’:
expand
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Attaching package: ‘IRanges’
The following objects are masked from ‘package:dplyr’:
collapse, desc, slice
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
The following objects are masked from ‘package:matrixStats’:
anyMissing, rowMedians
Loading required package: DelayedArray
Loading required package: BiocParallel
Attaching package: ‘DelayedArray’
The following objects are masked from ‘package:matrixStats’:
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following objects are masked from ‘package:base’:
aperm, apply, rowsumlibrary(stringr)
library(ggExtra)
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:Biobase’:
combine
The following object is masked from ‘package:BiocGenerics’:
combine
The following object is masked from ‘package:dplyr’:
combinelibrary(pheatmap)
library(reshape2)
Attaching package: ‘reshape2’
The following object is masked from ‘package:tidyr’:
smithslibrary("ggalluvial")give.n <- function(x){
return(c(y = mean(x), label = length(x)))
}SNP-ASE output See https://github.com/orionzhou/rnaseq/blob/master/output.md
rds <- readRDS("ase_1Oct19.rds")
ase.reads <- rds
cpm <- readRDS("cpm.rds")
lib.size.tab <- data.frame(cpm$tl)ase.reads$ratio <- ase.reads$allele1/(ase.reads$allele1 + ase.reads$allele2)
ase.reads$total.reads <- (ase.reads$allele1 + ase.reads$allele2)
ase.reads$contrast <- substr(ase.reads$sid,0,2)
ase.reads2 <- data.frame(ase.reads %>%
group_by(gid,contrast) %>%
dplyr::summarize(
mean.ratio = mean(ratio),
mean.count = mean(total.reads)))Gene key B73 reference gene model cross-reference relative to v4 Downloaded from MaizeGDB 2020/01/22, 11:32am Reformatted to remove header
gene.key <- read.table("gene_model_xref_v4c.txt",sep="\t",header=T,stringsAsFactors = F)
dup.gene <- subset(gene.key,duplicated(gene.key$v4_gene_model))
gene.key <- subset(gene.key,!gene.key$v4_gene_model %in% dup.gene$v4_gene_model)
gene.key.BW.syntelogs <- subset(gene.key,nchar(gene.key$W22.Zm00004b.1.) == 14)[,c(1:4,8,10,20,24)]
gene.key.bp.syntelogs <- subset(gene.key,nchar(gene.key$PH207.Zm00008a.1.) == 14)[,c(1:4,8,10,20,24)]
gene.key.BWP <- subset(gene.key,nchar(gene.key$W22.Zm00004b.1.) == 14 & nchar(gene.key$PH207.Zm00008a.1.) == 14)[,c(1:4,8,10,20,24)]Read in RER table and calculate RPM
imp.all <- read.table("combined_counts_all_concatinated_genomes_21Oct19.txt",header=T,stringsAsFactors = F) # update path
imp.all$ID <- str_replace_all(imp.all$ID,".v1.1","")
imp.all.rpm <- imp.all
for(i in 2:19){
imp.all.rpm[,i] <- imp.all[,i]/lib.size.tab[lib.size.tab$SampleID == colnames(imp.all[i]),"libSize"] * 1e6
}
imp.all2 <- subset(imp.all,!imp.all$ID %in% c("no_feature","ambiguous","too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
imp.all2$ID[grepl("gene",imp.all2$ID)] <- substr(imp.all2$ID[grepl("gene",imp.all2$ID)],6,19) # fix formattingSummary stats about libraries
imp.all.numerator <- subset(imp.all,!imp.all$ID %in% c("too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
imp.all.denominator <- subset(imp.all,!imp.all$ID %in% c("too_low_aQual","not_aligned")) # remove non-feature and ambiguous reads
summary.stats.libraries <- data.frame(cbind(colSums(imp.all.denominator[,2:19]),(colSums(imp.all.numerator[,2:19])/colSums(imp.all.denominator[,2:19]))*100))
names(summary.stats.libraries) <- c("Reads","Percent_Unique")
mean(summary.stats.libraries$Percent_Unique)[1] 16.78902imp.all.rpm2 <- subset(imp.all.rpm,!imp.all.rpm$ID %in% c("no_feature","ambiguous","too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
rownames(imp.all.rpm2) <- imp.all.rpm2$ID
imp.all.rpm2$ID <- NULL
rownames(imp.all.rpm2)[grepl("gene",rownames(imp.all.rpm2))] <- substr(rownames(imp.all.rpm2)[grepl("gene",rownames(imp.all.rpm2))],6,19) # fix formattingimp.all.rpm2$BW.mean <- rowMeans(imp.all.rpm2[,1:3]) #ignore this (I hard coded column numbers later)
imp.all.rpm2$WB.mean <- rowMeans(imp.all.rpm2[,4:6]) #ignore this (I hard coded column numbers later)
imp.all.rpm2$B_ratio <- imp.all.rpm2$BW.mean/(imp.all.rpm2$BW.mean + imp.all.rpm2$WB.mean) #ignore this (I hard coded column numbers later)
imp.all.rpm2$feature_type <- ifelse(nchar(rownames(imp.all.rpm2)) == 21, "TE","gene") # Label features as TEs or genes
imp.all.rpm2$genome <- ifelse(grepl("Zm00001d",rownames(imp.all.rpm2)),"B73",ifelse(grepl("Zm00004b",rownames(imp.all.rpm2)),"W22","PH207")) # Label which genome the feature annotation is from
table(imp.all.rpm2$genome)
B73 PH207 W22
370788 280880 354627 For each pair of genotypes, make the same columns and then do the math for the maternal preference ratio
imp.BvW.rpm <- imp.all.rpm2[,c(1:6,22:23)]
imp.BvW.rpm$contrast <- "BW"
imp.BvW.rpm$type <- ifelse(imp.BvW.rpm$genome == "B73","A","B")
imp.BvW.rpm$feature <- rownames(imp.BvW.rpm)
imp.BvP.rpm <- imp.all.rpm2[,c(7:12,22:23)]
imp.BvP.rpm$contrast <- "BP"
imp.BvP.rpm$type <- ifelse(imp.BvW.rpm$genome == "B73","A","B")
imp.BvP.rpm$feature <- rownames(imp.BvP.rpm)
imp.WvP.rpm <- imp.all.rpm2[,c(13:18,22:23)]
imp.WvP.rpm$contrast <- "WP"
imp.WvP.rpm$type <- ifelse(imp.BvW.rpm$genome == "W22","A","B")
imp.WvP.rpm$feature <- rownames(imp.WvP.rpm)
names(imp.BvW.rpm) <- names(imp.BvP.rpm) <- names(imp.WvP.rpm) <- c("AB1","AB2","AB3","BA1","BA2","BA3","feature_type","genome","contrast","type","feature")
imp.recips.rpm <- data.frame(rbind(imp.BvW.rpm,imp.BvP.rpm,imp.WvP.rpm))
imp.recips.rpm$A_mean <- rowMeans(imp.recips.rpm[,1:3])
imp.recips.rpm$B_mean <- rowMeans(imp.recips.rpm[,4:6])
imp.recips.rpm$maternal_preference <- ifelse(imp.recips.rpm$type == "A",imp.recips.rpm$A_mean/(imp.recips.rpm$A_mean + imp.recips.rpm$B_mean),imp.recips.rpm$B_mean/(imp.recips.rpm$A_mean + imp.recips.rpm$B_mean)) # maternal preferance calculationPlot the maternal preferance distributipns for each contrast, genome, and feature type
imp.recips.rpm.filter <- subset(imp.recips.rpm,imp.recips.rpm$A_mean >= 1 | imp.recips.rpm$B_mean >= 1)
ggplot(imp.recips.rpm.filter,aes(x=genome,y=maternal_preference,fill=genome))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_type~contrast,scales="free_x") + stat_summary(fun.data = give.n, geom = "text") 
Look at per-element or gene expression across development
dev.reads <- read.table("element_combined_counts_DevREDO_11Oct19.txt",header=T,stringsAsFactors = F,sep="\t")rownames(dev.reads) <- dev.reads$element
dev.reads$element <- NULL
dev.reads2 <- dev.reads[,grep("te.g",colnames(dev.reads),invert = T)]
names(dev.reads2) <- substr(names(dev.reads2),0,nchar(names(dev.reads2))-7)
dev.reads3 <- dev.reads2[,grep("DS",colnames(dev.reads2))]
dev.rpm <- dev.reads3
for(i in 1:ncol(dev.rpm)){
dev.rpm[,i] <- dev.reads3[,i]/sum(dev.reads3[,i]) * 1e6
}all.samples <- data.frame(names(dev.rpm))
all.samples$colname <- paste(names(dev.rpm))
all.samples <- separate(all.samples,names.dev.rpm.,c("tissue","details","experiment","genotype","rep"),sep="_")Expected 5 pieces. Additional pieces discarded in 5 rows [142, 185, 186, 187, 203].Make filter for potential maternal contamination MEGs (TE plus gene)
keep.columns <- c("endosperm_14dap_DS_B_1","endosperm_14dap_DS_B_2","endosperm_14dap_DS_B_3","seed_pericarp.18dap_DS_B_1","seed_pericarp.18dap_DS_B_2","seed_pericarp.18dap_DS_B_3")
B73.endoperi.stepflug <- subset(dev.rpm, rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns]
B73.endoperi.stepflug$mean.endo <- rowMeans(B73.endoperi.stepflug[,1:3])
B73.endoperi.stepflug$mean.peri <- rowMeans(B73.endoperi.stepflug[,4:6])
peri.preferred <- subset(B73.endoperi.stepflug,B73.endoperi.stepflug$mean.peri > 2 * B73.endoperi.stepflug$mean.endo)
nrow(peri.preferred)[1] 23355table(nchar(rownames(peri.preferred)))
14 21
13562 9793 peri.preferred.W22 <- subset(gene.key,gene.key$v4_gene_model %in% rownames(peri.preferred))[,"W22.Zm00004b.1."]
peri.preferred.PH207 <- subset(gene.key,gene.key$v4_gene_model %in% rownames(peri.preferred))[,"PH207.Zm00008a.1."]Use DEseq to call significance over a log2FC cutoff of 1 (2-fold, which is the expected ratio)
imp.all.de.bw <- imp.all2[,2:7]
rownames(imp.all.de.bw) <- imp.all2$ID
#imp.all.de.keep.bw <- subset(imp.all.de.bw,rowSums(imp.all.de.bw >0) >= 3)
imp.all.de.keep.bw <- subset(imp.all.de.bw,rowSums(imp.all.de.bw) >= 10)
#imp.all.de.keep.bw <- subset(imp.all.de.bw,rowMeans(imp.all.rpm2[,1:3]) >= 0.5 | rowMeans(imp.all.rpm2[,4:6]) >= 0.5)
sample.info.bw <- as.data.frame(cbind(paste(names(imp.all.de.keep.bw)),c("BW","BW","BW","WB","WB","WB")))
rownames(sample.info.bw) <- sample.info.bw[,1]
sample_list.bw <- sample.info.bw[,2]
names(sample.info.bw) <- c("sample.info.bw","sample_list")
dds.bw <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.bw,colData = sample.info.bw, design = ~ sample_list)
dds.bw <- DESeq(dds.bw)estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testingres.bw <- results(dds.bw, lfcThreshold=1, altHypothesis = "greaterAbs")
drawLines <- function() abline(h=c(-1,1),col="dodgerblue",lwd=2)
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.bw, ylim=ylim); drawLines()
Plot results with respect to RER
out.bw <- data.frame(res.bw)
plot.out.bw <- merge(imp.recips.rpm,out.bw,by.x="feature",by.y="row.names",all=F)
plot.out.bw$sig <- ifelse(plot.out.bw$padj < 0.05, "TRUE", "FALSE")
plot.out2.bw <- subset(plot.out.bw,!is.na(plot.out.bw$sig) & plot.out.bw$contrast %in% c("BW","WB"))
plot.out2.bw$category <- ifelse(plot.out2.bw$padj < 0.05 & plot.out2.bw$log2FoldChange < -1 & (plot.out2.bw$maternal_preference > .9 | plot.out2.bw$maternal_preference < .1), "up",ifelse(plot.out2.bw$padj < 0.05 & plot.out2.bw$log2FoldChange > 1 & (plot.out2.bw$maternal_preference > .9 | plot.out2.bw$maternal_preference < .1),"down","notDE"))
plot.out2.bw$imprint <- ifelse(plot.out2.bw$category =="up" & plot.out2.bw$genome == "B73","MEG",
ifelse(plot.out2.bw$category =="down" & plot.out2.bw$genome == "B73","PEG",
ifelse(plot.out2.bw$category =="down" & plot.out2.bw$genome == "W22","MEG",
ifelse(plot.out2.bw$category =="up" & plot.out2.bw$genome == "W22","PEG","no.imprint"))))
ggplot(plot.out2.bw,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 
plot.out2.bw$filter.imprint <- ifelse(plot.out2.bw$imprint == "MEG" & (plot.out2.bw$feature %in% rownames(peri.preferred) | plot.out2.bw$feature %in% peri.preferred.W22),"filtered.MEG",plot.out2.bw$imprint)Repeat for BP
imp.all.de.bp <- imp.all2[,8:13]
rownames(imp.all.de.bp) <- imp.all2$ID
#imp.all.de.keep.bp <- subset(imp.all.de.bp,rowSums(imp.all.de.bp >0) >= 3)
imp.all.de.keep.bp <- subset(imp.all.de.bp,rowSums(imp.all.de.bp) >= 10)
#imp.all.de.keep.bp <- subset(imp.all.de.bp,rowMeans(imp.all.rpm2[,7:9]) >= 0.5 | rowMeans(imp.all.rpm2[,10:12]) >= 0.5)
sample.info.bp <- as.data.frame(cbind(paste(names(imp.all.de.keep.bp)),c("BP","BP","BP","PB","PB","PB")))
rownames(sample.info.bp) <- sample.info.bp[,1]
sample_list.bp <- sample.info.bp[,2]
names(sample.info.bp) <- c("sample.info.bp","sample_list")
dds.bp <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.bp,colData = sample.info.bp, design = ~ sample_list)
dds.bp <- DESeq(dds.bp)estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testingres.bp <- results(dds.bp, lfcThreshold=1, altHypothesis = "greaterAbs")
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.bp, ylim=ylim); drawLines()
Plot results with respect to RER
out.bp <- data.frame(res.bp)
plot.out.bp <- merge(imp.recips.rpm,out.bp,by.x="feature",by.y="row.names",all=F)
plot.out.bp$sig <- ifelse(plot.out.bp$padj < 0.05, "TRUE", "FALSE")
plot.out2.bp <- subset(plot.out.bp,!is.na(plot.out.bp$sig) & plot.out.bp$contrast %in% c("BP","PB"))
plot.out2.bp$category <- ifelse(plot.out2.bp$padj < 0.05 & plot.out2.bp$log2FoldChange < -1 & (plot.out2.bp$maternal_preference > .9 | plot.out2.bp$maternal_preference < .1), "up",ifelse(plot.out2.bp$padj < 0.05 & plot.out2.bp$log2FoldChange > 1 & (plot.out2.bp$maternal_preference > .9 | plot.out2.bp$maternal_preference < .1),"down","notDE"))
plot.out2.bp$imprint <- ifelse(plot.out2.bp$category =="up" & plot.out2.bp$genome == "B73","MEG",
ifelse(plot.out2.bp$category =="down" & plot.out2.bp$genome == "B73","PEG",
ifelse(plot.out2.bp$category =="down" & plot.out2.bp$genome == "PH207","MEG",
ifelse(plot.out2.bp$category =="up" & plot.out2.bp$genome == "PH207","PEG","no.imprint"))))
ggplot(plot.out2.bp,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 
plot.out2.bp$filter.imprint <- ifelse(plot.out2.bp$imprint == "MEG" & (plot.out2.bp$feature %in% rownames(peri.preferred) | plot.out2.bp$feature %in% peri.preferred.PH207),"filtered.MEG",plot.out2.bp$imprint)Repeat for WP
imp.all.de.wp <- imp.all2[,14:19]
rownames(imp.all.de.wp) <- imp.all2$ID
#imp.all.de.keep.wp <- subset(imp.all.de.wp,rowSums(imp.all.de.wp >0) >= 3)
imp.all.de.keep.wp <- subset(imp.all.de.wp,rowSums(imp.all.de.wp) >= 10)
#imp.all.de.keep.wp <- subset(imp.all.de.wp,rowMeans(imp.all.rpm2[,13:15]) >= 0.5 | rowMeans(imp.all.rpm2[,16:18]) >= 0.5)
sample.info.wp <- as.data.frame(cbind(paste(names(imp.all.de.keep.wp)),c("WP","WP","WP","PW","PW","PW")))
rownames(sample.info.wp) <- sample.info.wp[,1]
sample_list.wp <- sample.info.wp[,2]
names(sample.info.wp) <- c("sample.info.wp","sample_list")
dds.wp <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.wp,colData = sample.info.wp, design = ~ sample_list)
dds.wp <- DESeq(dds.wp)estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testingres.wp <- results(dds.wp, lfcThreshold=1, altHypothesis = "greaterAbs")
drawLines <- function() abline(h=c(-1,1),col="dodgerblue",lwd=2)
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.wp, ylim=ylim); drawLines()
Plot results with respect to the RER
out.wp <- data.frame(res.wp)
plot.out.wp <- merge(imp.recips.rpm,out.wp,by.x="feature",by.y="row.names",all=F)
plot.out.wp$sig <- ifelse(plot.out.wp$padj < 0.05, "TRUE", "FALSE")
plot.out2.wp <- subset(plot.out.wp,!is.na(plot.out.wp$sig) & plot.out.wp$contrast %in% c("WP","PW"))
plot.out2.wp$category <- ifelse(plot.out2.wp$padj < 0.05 & plot.out2.wp$log2FoldChange < -1 & (plot.out2.wp$maternal_preference > .9 | plot.out2.wp$maternal_preference < .1), "up",ifelse(plot.out2.wp$padj < 0.05 & plot.out2.wp$log2FoldChange > 1 & (plot.out2.wp$maternal_preference > .9 | plot.out2.wp$maternal_preference < .1),"down","notDE"))
plot.out2.wp$imprint <- ifelse(plot.out2.wp$category =="up" & plot.out2.wp$genome == "PH207","MEG",
ifelse(plot.out2.wp$category =="down" & plot.out2.wp$genome == "PH207","PEG",
ifelse(plot.out2.wp$category =="down" & plot.out2.wp$genome == "W22","MEG",
ifelse(plot.out2.wp$category =="up" & plot.out2.wp$genome == "W22","PEG","no.imprint"))))
ggplot(plot.out2.wp,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 
plot.out2.wp$filter.imprint <- ifelse(plot.out2.wp$imprint == "MEG" & (plot.out2.wp$feature %in% peri.preferred.W22 | plot.out2.wp$feature %in% peri.preferred.PH207),"filtered.MEG",plot.out2.wp$imprint)Summarize imprinting calls
summary.bw <- data.frame(plot.out2.bw %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))
summary.bp <- data.frame(plot.out2.bp %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))
summary.wp <- data.frame(plot.out2.wp %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))
summary.combined <- rbind(summary.bw,summary.bp,summary.wp)
summary.combined genome feature_type filter.imprint contrast count
1 B73 gene filtered.MEG BW 19
2 B73 gene MEG BW 150
3 B73 gene no.imprint BW 14882
4 B73 gene PEG BW 73
5 B73 TE MEG BW 107
6 B73 TE no.imprint BW 1819
7 B73 TE PEG BW 7
8 W22 gene filtered.MEG BW 4
9 W22 gene MEG BW 117
10 W22 gene no.imprint BW 14450
11 W22 gene PEG BW 69
12 W22 TE MEG BW 95
13 W22 TE no.imprint BW 1773
14 W22 TE PEG BW 3
15 B73 gene filtered.MEG BP 15
16 B73 gene MEG BP 123
17 B73 gene no.imprint BP 16253
18 B73 gene PEG BP 79
19 B73 TE MEG BP 102
20 B73 TE no.imprint BP 2315
21 B73 TE PEG BP 10
22 PH207 gene filtered.MEG BP 19
23 PH207 gene MEG BP 96
24 PH207 gene no.imprint BP 14611
25 PH207 gene PEG BP 77
26 PH207 TE MEG BP 94
27 PH207 TE no.imprint BP 1406
28 PH207 gene filtered.MEG WP 2
29 PH207 gene MEG WP 81
30 PH207 gene no.imprint WP 14236
31 PH207 gene PEG WP 64
32 PH207 TE MEG WP 82
33 PH207 TE no.imprint WP 1667
34 PH207 TE PEG WP 2
35 W22 gene filtered.MEG WP 3
36 W22 gene MEG WP 108
37 W22 gene no.imprint WP 15558
38 W22 gene PEG WP 56
39 W22 TE MEG WP 91
40 W22 TE no.imprint WP 2636
41 W22 TE PEG WP 5summary.combined2 <- subset(summary.combined,summary.combined$filter.imprint %in% c("MEG","PEG"))
summary.combined2[summary.combined2$filter.imprint == "PEG","count"] <- -summary.combined2[summary.combined2$filter.imprint == "PEG","count"]
summary.combined2$label <- paste(summary.combined2$genome,summary.combined2$contrast,sep="_")
ggplot() + geom_bar(aes(x=label,y=count,fill=filter.imprint),data=summary.combined2,stat="identity",position = "identity") + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + facet_grid(.~feature_type,scales="free") + scale_fill_manual(values=c("magenta2","navy")) 
Average imprinted genes
imp.genes <- subset(summary.combined2,summary.combined2$feature_type == "gene")
imp.genes[imp.genes$filter.imprint == "PEG","count"] <- -imp.genes[imp.genes$filter.imprint == "PEG","count"]
imp.genes2 <- imp.genes%>% group_by(label) %>% dplyr::summarise(total.imprint = sum(count))
mean(imp.genes2$total.imprint)[1] 182.1667mean(subset(imp.genes,imp.genes$filter.imprint == "MEG")[,"count"])[1] 112.5mean(subset(imp.genes,imp.genes$filter.imprint == "PEG")[,"count"])[1] 69.66667mean(subset(summary.combined2,summary.combined2$feature_type == "TE" & summary.combined2$filter.imprint == "MEG")[,"count"])[1] 95.16667Use to make venns - B73 vs W22
compare.bw.syntelogs <- subset(plot.out2.bw,plot.out2.bw$feature_type == "gene")
compare.bw.syntelogs$syntelog <- ifelse(compare.bw.syntelogs$feature %in% gene.key.BW.syntelogs$v4_gene_model | compare.bw.syntelogs$feature %in% gene.key.BW.syntelogs$W22.Zm00004b.1., "syntelog","non.syntelog")
table(compare.bw.syntelogs[,c("genome","filter.imprint","syntelog")]), , syntelog = non.syntelog
filter.imprint
genome filtered.MEG MEG no.imprint PEG
B73 2 107 2893 13
W22 0 89 2733 11
, , syntelog = syntelog
filter.imprint
genome filtered.MEG MEG no.imprint PEG
B73 17 43 11989 60
W22 4 28 11717 58compare.bw.syntelogs.b73 <- merge(subset(compare.bw.syntelogs,compare.bw.syntelogs$syntelog == "syntelog" & compare.bw.syntelogs$genome == "B73"),gene.key.BW.syntelogs,by.x="feature",by.y="v4_gene_model",all.x=T)
compare.bw.syntelogs.w22 <- subset(compare.bw.syntelogs,compare.bw.syntelogs$syntelog == "syntelog" & compare.bw.syntelogs$genome == "W22")
compare.bw.syntelogs.merge <- merge(compare.bw.syntelogs.b73,compare.bw.syntelogs.w22,by.x = "W22.Zm00004b.1.",by.y="feature",all=T)
names(compare.bw.syntelogs.merge) <- gsub(".x",".B73",names(compare.bw.syntelogs.merge),fixed = T)
names(compare.bw.syntelogs.merge) <- gsub(".y",".W22",names(compare.bw.syntelogs.merge),fixed = T)
compare.bw.syntelogs.merge$imprint.B73[is.na(compare.bw.syntelogs.merge$imprint.B73)] <- "no.imprint"
compare.bw.syntelogs.merge$imprint.W22[is.na(compare.bw.syntelogs.merge$imprint.W22)] <- "no.imprint"
compare.bw.syntelogs.merge$filter.imprint.B73[is.na(compare.bw.syntelogs.merge$filter.imprint.B73)] <- "no.imprint"
compare.bw.syntelogs.merge$filter.imprint.W22[is.na(compare.bw.syntelogs.merge$filter.imprint.W22)] <- "no.imprint"
table(compare.bw.syntelogs.merge[,c("filter.imprint.B73","filter.imprint.W22")]) filter.imprint.W22
filter.imprint.B73 filtered.MEG MEG no.imprint PEG
filtered.MEG 0 0 17 0
MEG 0 17 26 0
no.imprint 4 11 13088 19
PEG 0 0 21 39Use to make venns - B73 vs PH207
compare.bp.syntelogs <- subset(plot.out2.bp,plot.out2.bp$feature_type == "gene")
compare.bp.syntelogs$syntelog <- ifelse(compare.bp.syntelogs$feature %in% gene.key.bp.syntelogs$v4_gene_model | compare.bp.syntelogs$feature %in% gene.key.bp.syntelogs$PH207.Zm00008a.1., "syntelog","non.syntelog")
table(compare.bp.syntelogs[,c("genome","filter.imprint","syntelog")]), , syntelog = non.syntelog
filter.imprint
genome filtered.MEG MEG no.imprint PEG
B73 2 65 1737 9
PH207 0 44 2161 5
, , syntelog = syntelog
filter.imprint
genome filtered.MEG MEG no.imprint PEG
B73 13 58 14516 70
PH207 19 52 12450 72compare.bp.syntelogs.b73 <- merge(subset(compare.bp.syntelogs,compare.bp.syntelogs$syntelog == "syntelog" & compare.bp.syntelogs$genome == "B73"),gene.key.bp.syntelogs,by.x="feature",by.y="v4_gene_model",all.x=T)
compare.bp.syntelogs.PH207 <- subset(compare.bp.syntelogs,compare.bp.syntelogs$syntelog == "syntelog" & compare.bp.syntelogs$genome == "PH207")
compare.bp.syntelogs.merge <- merge(compare.bp.syntelogs.b73,compare.bp.syntelogs.PH207,by.x = "PH207.Zm00008a.1.",by.y="feature",all=T)
names(compare.bp.syntelogs.merge) <- gsub(".x",".B73",names(compare.bp.syntelogs.merge),fixed = T)
names(compare.bp.syntelogs.merge) <- gsub(".y",".PH207",names(compare.bp.syntelogs.merge),fixed = T)
compare.bp.syntelogs.merge$imprint.B73[is.na(compare.bp.syntelogs.merge$imprint.B73)] <- "no.imprint"
compare.bp.syntelogs.merge$imprint.PH207[is.na(compare.bp.syntelogs.merge$imprint.PH207)] <- "no.imprint"
compare.bp.syntelogs.merge$filter.imprint.B73[is.na(compare.bp.syntelogs.merge$filter.imprint.B73)] <- "no.imprint"
compare.bp.syntelogs.merge$filter.imprint.PH207[is.na(compare.bp.syntelogs.merge$filter.imprint.PH207)] <- "no.imprint"
table(compare.bp.syntelogs.merge[,c("filter.imprint.B73","filter.imprint.PH207")]) filter.imprint.PH207
filter.imprint.B73 filtered.MEG MEG no.imprint PEG
filtered.MEG 1 0 11 1
MEG 0 19 38 1
no.imprint 18 31 15496 30
PEG 0 2 26 42matTE venns
te.anno.compare <- read.table("non-redundant_TEs_by_annptation_4genomes_24Jan20.txt",header = T,stringsAsFactors = F)B73 vs W22 TEs
shared.BW <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation == 21) & te.anno.compare$W22 == "present" & nchar(te.anno.compare$W22.annotation == 21))
compare.bw.TE <- subset(plot.out2.bw,plot.out2.bw$feature_type == "TE" & plot.out2.bw$filter.imprint == "MEG")
compare.bw.TE$shared <- ifelse(compare.bw.TE$feature %in% shared.BW$B73.annotation | compare.bw.TE$feature %in% shared.BW$W22.annotation, "shared","variable")
table(compare.bw.TE[,c("genome","filter.imprint","shared")]), , shared = shared
filter.imprint
genome MEG
B73 9
W22 17
, , shared = variable
filter.imprint
genome MEG
B73 98
W22 78compare.bw.TE.b <- merge(subset(compare.bw.TE,compare.bw.TE$shared == "shared" & compare.bw.TE$genome == "B73"),shared.BW[,c("B73.annotation","W22.annotation")],by.x="feature",by.y="B73.annotation",all.x=T)
compare.bw.TE.w <- subset(compare.bw.TE,compare.bw.TE$shared == "shared" & compare.bw.TE$genome == "W22")
compare.bw.TE.merge <- merge(compare.bw.TE.b, compare.bw.TE.w, by.x="W22.annotation",by.y="feature",all=T)
names(compare.bw.TE.merge) <- gsub(".x",".B73",names(compare.bw.TE.merge),fixed = T)
names(compare.bw.TE.merge) <- gsub(".y",".W22",names(compare.bw.TE.merge),fixed = T)
compare.bw.TE.merge$filter.imprint.B73[is.na(compare.bw.TE.merge$filter.imprint.B73)] <- "no.imprint"
compare.bw.TE.merge$filter.imprint.W22[is.na(compare.bw.TE.merge$filter.imprint.W22)] <- "no.imprint"
table(compare.bw.TE.merge[,c("filter.imprint.B73","filter.imprint.W22")]) filter.imprint.W22
filter.imprint.B73 MEG no.imprint
MEG 4 5
no.imprint 13 0B73 vs PH207 TEs
shared.bp <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation) == 21 & te.anno.compare$PH207 == "present" & nchar(te.anno.compare$PH207.annotation) == 21)
compare.bp.TE <- subset(plot.out2.bp,plot.out2.bp$feature_type == "TE" & plot.out2.bp$filter.imprint == "MEG")
compare.bp.TE$shared <- ifelse(compare.bp.TE$feature %in% shared.bp$B73.annotation | compare.bp.TE$feature %in% shared.bp$P207.annotation, "shared","variable")
table(compare.bp.TE[,c("genome","filter.imprint","shared")]), , shared = shared
filter.imprint
genome MEG
B73 11
PH207 0
, , shared = variable
filter.imprint
genome MEG
B73 91
PH207 94compare.bp.TE.b <- merge(subset(compare.bp.TE,compare.bp.TE$shared == "shared" & compare.bp.TE$genome == "B73"),shared.bp[,c("B73.annotation","PH207.annotation")],by.x="feature",by.y="B73.annotation",all.x=T)
compare.bp.TE.w <- subset(compare.bp.TE,compare.bp.TE$shared == "shared" & compare.bp.TE$genome == "PH207")
compare.bp.TE.merge <- merge(compare.bp.TE.b, compare.bp.TE.w, by.x="PH207.annotation",by.y="feature",all=T)
names(compare.bp.TE.merge) <- gsub(".x",".B73",names(compare.bp.TE.merge),fixed = T)
names(compare.bp.TE.merge) <- gsub(".y",".PH207",names(compare.bp.TE.merge),fixed = T)
compare.bp.TE.merge$filter.imprint.B73[is.na(compare.bp.TE.merge$filter.imprint.B73)] <- "no.imprint"
compare.bp.TE.merge$filter.imprint.PH207[is.na(compare.bp.TE.merge$filter.imprint.PH207)] <- "no.imprint"
table(compare.bp.TE.merge[,c("filter.imprint.B73","filter.imprint.PH207")]) filter.imprint.PH207
filter.imprint.B73 no.imprint
MEG 11How many MEGs called in only one direction were due to cutoffs and no data? Use B73 by W22 contrast for numbers
B73.only.MEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "no.imprint")
B73.only.MEG.bw$category <- ifelse(is.na(B73.only.MEG.bw$maternal_preference.W22),"low.coverage",
ifelse(B73.only.MEG.bw$maternal_preference.W22 > 0.75,"RER>0.75","no.imprint"))
B73.only.MEG.bw$group <- "B73.MEG.BW"
W22.only.MEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bw.syntelogs.merge$filter.imprint.W22 == "MEG")
W22.only.MEG.bw$category <- ifelse(is.na(W22.only.MEG.bw$maternal_preference.B73),"low.coverage",
ifelse(W22.only.MEG.bw$maternal_preference.B73 > 0.75,"RER>0.75","no.imprint"))
W22.only.MEG.bw$group <- "W22.MEG.BW"
B73.only.MEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "no.imprint")
B73.only.MEG.bp$category <- ifelse(is.na(B73.only.MEG.bp$maternal_preference.PH207),"low.coverage",
ifelse(B73.only.MEG.bp$maternal_preference.PH207 > 0.75,"RER>0.75","no.imprint"))
B73.only.MEG.bp$group <- "B73.MEG.BP"
PH207.only.MEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "MEG")
PH207.only.MEG.bp$category <- ifelse(is.na(PH207.only.MEG.bp$maternal_preference.B73),"low.coverage",
ifelse(PH207.only.MEG.bp$maternal_preference.B73 > 0.75,"RER>0.75","no.imprint"))
PH207.only.MEG.bp$group <- "PH207.MEG.BP"
not.enough.mat.bias <- sum(nrow(subset(B73.only.MEG.bw,B73.only.MEG.bw$maternal_preference.W22 > 0.75)),
nrow(subset(W22.only.MEG.bw,W22.only.MEG.bw$maternal_preference.B73 > 0.75)))
total.non.overlap <- sum(nrow(B73.only.MEG.bw),nrow(W22.only.MEG.bw))
not.enough.mat.bias / total.non.overlap * 100[1] 27.02703not.enough.mat.data <- sum(nrow(subset(B73.only.MEG.bw,is.na(B73.only.MEG.bw$maternal_preference.W22))),
nrow(subset(W22.only.MEG.bw,is.na(W22.only.MEG.bw$maternal_preference.B73))))
not.enough.mat.data / total.non.overlap * 100[1] 32.43243MEG.non.overlap <- rbind(B73.only.MEG.bw[,c("group","category")],W22.only.MEG.bw[,c("group","category")],B73.only.MEG.bp[,c("group","category")],PH207.only.MEG.bp[,c("group","category")])
plot.MEG.non.overlap <- data.frame(table(MEG.non.overlap))
plot.MEG.non.overlap$category <- factor(plot.MEG.non.overlap$category, levels=c("no.imprint","low.coverage","RER>0.75"))
plot.MEG.non.overlap$group <- factor(plot.MEG.non.overlap$group,levels=c("B73.MEG.BW","W22.MEG.BW","B73.MEG.BP","PH207.MEG.BP"))
ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.MEG.non.overlap,stat="identity",position=position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Reds")
What about PEGs? Use B73 by W22 contrast for numbers
B73.only.PEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "no.imprint")
B73.only.PEG.bw$category <- ifelse(is.na(B73.only.PEG.bw$maternal_preference.W22),"low.coverage",
ifelse(B73.only.PEG.bw$maternal_preference.W22 < 0.25,"RER<0.25","no.imprint"))
B73.only.PEG.bw$group <- "B73.PEG.BW"
W22.only.PEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bw.syntelogs.merge$filter.imprint.W22 == "PEG")
W22.only.PEG.bw$category <- ifelse(is.na(W22.only.PEG.bw$maternal_preference.B73),"low.coverage",
ifelse(W22.only.PEG.bw$maternal_preference.B73 < 0.25,"RER<0.25","no.imprint"))
W22.only.PEG.bw$group <- "W22.PEG.BW"
B73.only.PEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "no.imprint")
B73.only.PEG.bp$category <- ifelse(is.na(B73.only.PEG.bp$maternal_preference.PH207),"low.coverage",
ifelse(B73.only.PEG.bp$maternal_preference.PH207 < 0.25,"RER<0.25","no.imprint"))
B73.only.PEG.bp$group <- "B73.PEG.BP"
PH207.only.PEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "PEG")
PH207.only.PEG.bp$category <- ifelse(is.na(PH207.only.PEG.bp$maternal_preference.B73),"low.coverage",
ifelse(PH207.only.PEG.bp$maternal_preference.B73 < 0.25,"RER<0.25","no.imprint"))
PH207.only.PEG.bp$group <- "PH207.PEG.BP"
not.enough.pat.bias <- sum(nrow(subset(B73.only.PEG.bw,B73.only.PEG.bw$maternal_preference.W22 < 0.25)),
nrow(subset(W22.only.PEG.bw,W22.only.PEG.bw$maternal_preference.B73 > 0.25)))
total.non.overlap.pat <- sum(nrow(B73.only.PEG.bw),nrow(W22.only.PEG.bw))
not.enough.pat.bias / total.non.overlap.pat * 100[1] 57.5not.enough.pat.data <- sum(nrow(subset(B73.only.PEG.bw,is.na(B73.only.PEG.bw$maternal_preference.W22))),
nrow(subset(W22.only.PEG.bw,is.na(W22.only.PEG.bw$maternal_preference.B73))))
not.enough.pat.data / total.non.overlap.pat * 100[1] 7.5PEG.non.overlap <- rbind(B73.only.PEG.bw[,c("group","category")],W22.only.PEG.bw[,c("group","category")],B73.only.PEG.bp[,c("group","category")],PH207.only.PEG.bp[,c("group","category")])
plot.PEG.non.overlap <- data.frame(table(PEG.non.overlap))
plot.PEG.non.overlap$category <- factor(plot.PEG.non.overlap$category, levels=c("no.imprint","low.coverage","RER<0.25"))
plot.PEG.non.overlap$group <- factor(plot.PEG.non.overlap$group,levels=c("B73.PEG.BW","W22.PEG.BW","B73.PEG.BP","PH207.PEG.BP"))
ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.PEG.non.overlap,stat="identity",position=position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Blues")
Combined
a <- ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.MEG.non.overlap,stat="identity",position=position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Reds")
b <- ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.PEG.non.overlap,stat="identity",position=position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Blues")
grid.arrange(a,b,nrow=1)
Create file of BW calls
tmp.meg <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "MEG")
tmp.peg <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "PEG")
tmp3 <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 != compare.bw.syntelogs.merge$filter.imprint.W22)
bw.out.share <- plot.out2.bw
bw.out.share$syntelog <- ifelse(bw.out.share$feature_type == "TE", "na.te",
ifelse(bw.out.share$feature %in% gene.key.BW.syntelogs$v4_gene_model
| bw.out.share$feature %in% gene.key.BW.syntelogs$W22.Zm00004b.1., "syntelog","non.syntelog"))
bw.out.share$imprinted.both <- ifelse(bw.out.share$syntelog == "na.te" | bw.out.share$syntelog == "non.syntelog",bw.out.share$syntelog,
ifelse(bw.out.share$feature %in% tmp.meg$feature | bw.out.share$feature %in% tmp.meg$W22.Zm00004b.1.,"both.megs",
ifelse(bw.out.share$feature %in% tmp.peg$feature | bw.out.share$feature %in% tmp.peg$W22.Zm00004b.1.,"both.pegs",
ifelse(bw.out.share$feature %in% tmp3$feature | bw.out.share$feature %in% tmp3$W22.Zm00004b.1.,"inconsistent.imprint","no.imprint"))))tmp1 <- bw.out.share[bw.out.share$genome == "B73",c("feature","filter.imprint","syntelog","imprinted.both")]
names(tmp1)[2:4] <- c("filter.imprint.BW","syntelog.BW","imprinted.both.BW")
tmp2 <- plot.out2.bp[plot.out2.bp$genome == "B73",c("feature","filter.imprint")]
names(tmp2)[2] <- "filter.imprint.BP"
tmp2$syntelog.BP <- ifelse(nchar(tmp2$feature) == 21, "na.te",
ifelse(tmp2$feature %in% gene.key.bp.syntelogs$v4_gene_model, "syntelog","non.syntelog"))
b.both.contrasts <- merge(tmp1,tmp2,by="feature",all=T)
b.both.contrasts$filter.imprint.BW[is.na(b.both.contrasts$filter.imprint.BW)] <- "not.detected"
b.both.contrasts$filter.imprint.BP[is.na(b.both.contrasts$filter.imprint.BP)] <- "not.detected"
b.both.contrasts[is.na(b.both.contrasts)] <- "NA"
b.both.contrasts$feature_type <- ifelse(nchar(b.both.contrasts$feature) == 21, "TE","gene")
b.both.contrasts$imprint <- ifelse(b.both.contrasts$feature_type == "TE" & (b.both.contrasts$filter.imprint.BP == "MEG" | b.both.contrasts$filter.imprint.BW == "MEG"),"matTE",
ifelse(b.both.contrasts$filter.imprint.BP == "MEG" | b.both.contrasts$filter.imprint.BW == "MEG","MEG",
ifelse(b.both.contrasts$feature_type == "gene" & (b.both.contrasts$filter.imprint.BP == "PEG" | b.both.contrasts$filter.imprint.BW == "PEG"),"PEG","no.imprint")))
b.both.contrasts$syntelog <- ifelse(b.both.contrasts$feature == "TE", "na.TE",
ifelse(b.both.contrasts$feature %in% gene.key.BWP$v4_gene_model, "conserved.maize","variable"))
table(b.both.contrasts[,c("filter.imprint.BW","filter.imprint.BP")]) filter.imprint.BP
filter.imprint.BW filtered.MEG MEG no.imprint not.detected PEG
filtered.MEG 3 0 14 2 0
MEG 0 135 75 47 0
no.imprint 9 54 14819 1792 27
not.detected 3 36 3632 0 20
PEG 0 0 28 10 42What proportion of the syntelog vs non-syntelogs agree across contrasts?
b.both.contrasts$agreement <- ifelse(b.both.contrasts$filter.imprint.BP == b.both.contrasts$filter.imprint.BW, "matched.call","mismatched_call")
syntelog.agreement <- data.frame(b.both.contrasts %>% group_by(syntelog,agreement,imprint) %>% dplyr::summarise(features = n()))
ggplot() + geom_bar(aes(x=syntelog,y=features,fill=agreement),data=subset(syntelog.agreement,syntelog.agreement$imprint != "no.imprint"),stat="identity",position = position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + facet_grid(.~imprint,scales="free")
Compare to SNP-ASE method
tmp <- subset(ase.reads2,ase.reads2$contrast == "BW" & ase.reads2$mean.count >= 10)
names(tmp) <- c("gid","ase.contrast","ase.BW.ratio","ase.BW.count")
bw.merge <- merge(plot.out2.bw,tmp,by.x="feature",by.y="gid",all.x=T)
tmp2 <- subset(ase.reads2,ase.reads2$contrast == "WB" & ase.reads2$mean.count >= 10)
names(tmp2) <- c("gid","ase.contrast","ase.WB.ratio","ase.WB.count")
bw.merge2 <- merge(bw.merge,tmp2,by.x="feature",by.y="gid",all.x=T)
bw.merge2$genotype.biased <- ifelse(bw.merge2$ase.BW.ratio >= 0.9 & bw.merge2$ase.WB.ratio <= 0.1, "B73.biased",ifelse(bw.merge2$ase.WB.ratio >= 0.9 & bw.merge2$ase.BW.ratio <= 0.1,"W22.biased","fine"))
bw.merge2$group <- factor(ifelse(bw.merge2$genotype.biased == "fine",bw.merge2$filter.imprint,bw.merge2$genotype.biased),levels = c("B73.biased","W22.biased","MEG","PEG","no.imprint"))
a <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.BW.ratio,y=maternal_preference)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE BxW") + ylab("RER") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")Removed 23257 rows containing missing values (geom_point).b <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.WB.ratio,y=maternal_preference)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE WxB") + ylab("RER") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")Removed 23935 rows containing missing values (geom_point).c <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.BW.ratio,y=ase.WB.ratio)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE WxB") + ylab("SNP-ASE BxW") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")Removed 23997 rows containing missing values (geom_point).grid.arrange(a,b,c,nrow=2)
B73 vs W22
a <- ggplot(bw.merge2,aes(x=ase.BW.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("SNP-ASE BxW") + ylab("RER")
b <- ggplot(bw.merge2,aes(x=ase.WB.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("SNP-ASE WxB") + ylab("RER")
grid.arrange(a,b,nrow=1)
Compare B73 vs PH207
bp.merged.out <- merge(plot.out2.bp,subset(ase.reads2,ase.reads2$contrast == "BP"),by.x="feature",by.y="gid",all=F)
bp.merged.out2 <- subset(bp.merged.out,bp.merged.out$mean.count >= 10)
a <- ggplot(bp.merged.out2,aes(x=mean.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("Mean SNP Maternal Ratio BxW") + ylab("Maternal Preference Ratio")
bp.merged.out3 <- merge(plot.out2.bp,subset(ase.reads2,ase.reads2$contrast == "PB"),by.x="feature",by.y="gid",all=F)
bp.merged.out4 <- subset(bp.merged.out3,bp.merged.out3$mean.count >= 10)
b <- ggplot(bp.merged.out4,aes(x=mean.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("Mean SNP Maternal Ratio WxB") + ylab("Maternal Preference Ratio")
grid.arrange(a,b,nrow=1)
Plot just genes
plot.just.genes <- rbind(subset(plot.out2.bw,plot.out2.bw$feature_type == "gene"),subset(plot.out2.bp,plot.out2.bp$feature_type == "gene"),subset(plot.out2.wp,plot.out2.wp$feature_type == "gene"))
ggplot(plot.just.genes,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~contrast) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 
table(plot.just.genes[,c("genome","contrast","imprint")]), , imprint = MEG
contrast
genome BP BW WP
B73 138 169 0
PH207 115 0 83
W22 0 121 111
, , imprint = no.imprint
contrast
genome BP BW WP
B73 16253 14882 0
PH207 14611 0 14236
W22 0 14450 15558
, , imprint = PEG
contrast
genome BP BW WP
B73 79 73 0
PH207 77 0 64
W22 0 69 56meg.genes <- subset(b.both.contrasts,b.both.contrasts$imprint == "MEG")
length(meg.genes$feature[duplicated(meg.genes$feature)])[1] 0length(unique(meg.genes$feature))[1] 202peg.genes <- subset(b.both.contrasts,b.both.contrasts$imprint == "PEG")
length(peg.genes$feature[duplicated(peg.genes$feature)])[1] 0length(unique(peg.genes$feature))[1] 111meg.tes <- subset(b.both.contrasts,b.both.contrasts$imprint == "matTE")
length(meg.tes$feature[duplicated(meg.tes$feature)])[1] 0length(unique(meg.tes$feature))[1] 145Check endosperm vs pericarp expression, especially for MEGs
unfiltered.meg.genes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "gene" & (b.both.contrasts$filter.imprint.BW == "MEG" | b.both.contrasts$filter.imprint.BW == "filtered.MEG"))
imprinted.meg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% unfiltered.meg.genes$feature & rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns])
pheatmap((imprinted.meg.4heat/rowMaxs(imprinted.meg.4heat)),border_color = NA,fontsize = 8,cluster_cols = F)
a <- subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% rownames(peri.preferred))/rowMaxs(subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% rownames(peri.preferred)))
b <- subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% rownames(peri.preferred))/rowMaxs(subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% rownames(peri.preferred)))
imprinted.meg.4heat.v2 <- rbind(a[order(rowMeans(a),decreasing = T),],b[order(rowMeans(b),decreasing = T),])
pheatmap((imprinted.meg.4heat.v2/rowMaxs(imprinted.meg.4heat.v2)),border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,show_rownames = F,gaps_row = nrow(a))
Check for TEs
peri.te.heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.tes$feature & rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns])
pheatmap((peri.te.heat/rowMaxs(peri.te.heat)),border_color = NA,fontsize = 8,cluster_cols = F)
peri.te.heat.v2 <- peri.te.heat/rowMaxs(peri.te.heat)
pheatmap((peri.te.heat.v2[order(rowMeans(peri.te.heat.v2),decreasing = T),]),border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,show_rownames = F)
Plot just TEs
plot.just.tes <- rbind(subset(plot.out2.bw,plot.out2.bw$feature_type == "TE"),subset(plot.out2.bp,plot.out2.bp$feature_type == "TE"),subset(plot.out2.wp,plot.out2.wp$feature_type == "TE"))
ggplot(plot.just.tes,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~contrast) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 
table(plot.just.tes[,c("genome","contrast","filter.imprint")]), , filter.imprint = MEG
contrast
genome BP BW WP
B73 102 107 0
PH207 94 0 82
W22 0 95 91
, , filter.imprint = no.imprint
contrast
genome BP BW WP
B73 2315 1819 0
PH207 1406 0 1667
W22 0 1773 2636
, , filter.imprint = PEG
contrast
genome BP BW WP
B73 10 7 0
PH207 0 0 2
W22 0 3 5Make distribution of all based on which libraries got included in DE calls
imp.recips.rpm2 <- data.frame(rbind(subset(imp.BvW.rpm,imp.BvW.rpm$feature %in% rownames(imp.all.de.keep.bw)),subset(imp.BvP.rpm,imp.BvP.rpm$feature %in% rownames(imp.all.de.keep.bp)),subset(imp.WvP.rpm,imp.WvP.rpm$feature %in% rownames(imp.all.de.keep.wp))))
imp.recips.rpm2$A_mean <- rowMeans(imp.recips.rpm2[,1:3])
imp.recips.rpm2$B_mean <- rowMeans(imp.recips.rpm2[,4:6])
imp.recips.rpm2$maternal_preference <- ifelse(imp.recips.rpm2$type == "A",imp.recips.rpm2$A_mean/(imp.recips.rpm2$A_mean + imp.recips.rpm2$B_mean),imp.recips.rpm2$B_mean/(imp.recips.rpm2$A_mean + imp.recips.rpm2$B_mean)) # maternal preferance calculation
ggplot(imp.recips.rpm2,aes(x=genome,y=maternal_preference,fill=genome)) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_type~contrast,scales="free_x") + stat_summary(fun.data = give.n, geom = "text") 
Plot as stacked bars instead
imp.recips.rpm2$mat.category <- factor(ifelse(imp.recips.rpm2$maternal_preference > 0.9, "strong.maternal",
ifelse(imp.recips.rpm2$maternal_preference < 0.1, "strong.paternal",
ifelse(imp.recips.rpm2$maternal_preference > 0.8, "moderate.maternal",
ifelse(imp.recips.rpm2$maternal_preference < 0.2, "moderate.paternal", "biparental")))),
levels = c("strong.maternal","moderate.maternal","biparental","moderate.paternal","strong.paternal"))
imp.recips.rpm2$feature_category <- factor(ifelse(imp.recips.rpm2$feature_type == "TE","TE",
ifelse(imp.recips.rpm2$feature %in% gene.key.BWP$v4_gene_model
| imp.recips.rpm2$feature %in% gene.key.BWP$W22.Zm00004b.1.
| imp.recips.rpm2$feature %in% gene.key.BWP$PH207.Zm00008a.1., "maize.syntenic.gene","maize.nonsyntenic.gene")),
levels=c("maize.syntenic.gene","maize.nonsyntenic.gene","TE"))
stack.mat.category <- data.frame(imp.recips.rpm2 %>% group_by(feature_category,genome,contrast,mat.category) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(proportion = n/sum(n)))
stack.mat.category$sample <- paste(stack.mat.category$genome,stack.mat.category$contrast,sep="_")
ggplot() + geom_bar(aes(x=sample,y=proportion,fill=mat.category),data=stack.mat.category,stat="identity",position = position_fill()) + theme_classic() + scale_fill_manual(values=c("magenta","magenta4","darkgray","navy","blue")) + facet_wrap(.~feature_category,scales="free") + theme(axis.text.x = element_text(angle=90, hjust=1)) 
proportion.strong - syntenic genes
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "maize.syntenic.gene" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)[1] 0.02174116TEs
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "TE" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)[1] 0.117614nonsyntenic genes
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "maize.nonsyntenic.gene" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)[1] 0.06492446ggplot(imp.recips.rpm2,aes(x=genome,y=maternal_preference,fill=genome)) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_category~contrast,scales="free_x") + stat_summary(fun.data = give.n, geom = "text") 
gene.key.BWP$B.imp.BW <- ifelse(gene.key.BWP$v4_gene_model %in% subset(plot.out2.bw,plot.out2.bw$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$v4_gene_model %in% plot.out2.bw$feature,"not.imprinted","NA"))
gene.key.BWP$W.imp.BW <- ifelse(gene.key.BWP$W22.Zm00004b.1. %in% subset(plot.out2.bw,plot.out2.bw$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$W22.Zm00004b.1. %in% plot.out2.bw$feature,"not.imprinted","NA"))
gene.key.BWP$B.imp.BP <- ifelse(gene.key.BWP$v4_gene_model %in% subset(plot.out2.bp,plot.out2.bp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$v4_gene_model %in% plot.out2.bp$feature,"not.imprinted","NA"))
gene.key.BWP$P.imp.BP <- ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% subset(plot.out2.bp,plot.out2.bp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% plot.out2.bp$feature,"not.imprinted","NA"))
gene.key.BWP$P.imp.WP <- ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% subset(plot.out2.wp,plot.out2.wp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% plot.out2.wp$feature,"not.imprinted","NA"))
gene.key.BWP$W.imp.WP <- ifelse(gene.key.BWP$W22.Zm00004b.1. %in% subset(plot.out2.wp,plot.out2.wp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$W22.Zm00004b.1. %in% plot.out2.wp$feature,"not.imprinted","NA"))Look at endosperm-specific expression vs constituative expression
endo.seed.columns <- c(names(dev.rpm)[grep("endosperm",names(dev.rpm))],names(dev.rpm)[grep("seed",names(dev.rpm))])
other.columns <- names(dev.rpm)[!names(dev.rpm) %in% endo.seed.columns]
other.columns <- other.columns[order(other.columns)]
endo.preferred <- rownames(subset(dev.rpm,rowSums(dev.rpm[,endo.seed.columns])/rowSums(dev.rpm) > 0.6))
plot.column.order <- c(endo.seed.columns,other.columns)Summarize features further
b.both.contrasts$expression.type <- ifelse(b.both.contrasts$feature %in% endo.preferred, "endo.preferred","constitutive")MEG genes
imprinted.meg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.genes$feature))
a <- subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% endo.preferred)
b <- subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% endo.preferred)
c <- rbind(a,b)
pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of MEGs")
meg.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of MEGs")
PEG genes
imprinted.peg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% peg.genes$feature))
a <- subset(imprinted.peg.4heat,rownames(imprinted.peg.4heat) %in% endo.preferred)
b <- subset(imprinted.peg.4heat,!rownames(imprinted.peg.4heat) %in% endo.preferred)
c <- rbind(a,b)
pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of PEGs")
peg.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of PEGs")
maternal TEs
imprinted.te.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.tes$feature))
a <- subset(imprinted.te.4heat,rownames(imprinted.te.4heat) %in% endo.preferred)
b <- subset(imprinted.te.4heat,!rownames(imprinted.te.4heat) %in% endo.preferred)
c <- rbind(a,b)
pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = T, main = "Developmental Expression of matTEs")
matTE.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = T, main = "Developmental Expression of matTEs")
Stacked bar with imprinted features, syntelog, expression.type
of.interest <- subset(b.both.contrasts,b.both.contrasts$imprint != "no.imprint")
of.interest2 <- data.frame(of.interest %>% group_by(imprint,syntelog,expression.type) %>% dplyr::summarise(features = n()))
of.interest2$type <- factor(ifelse(of.interest2$imprint == "matTE", "matTE",paste(of.interest2$imprint,of.interest2$syntelog,sep="_")),
levels=c("PEG_conserved.maize","PEG_variable","MEG_conserved.maize","MEG_variable","matTE"))
of.interest2$expression.type <- factor(of.interest2$expression.type,levels=c("endo.preferred","constitutive"))
ggplot() + geom_bar(aes(x=type,y=features,fill=expression.type),data=of.interest2,stat="identity",position = position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_manual(values=c("darkorange","honeydew4")) 
Binding sites Find binding sites from Batista et al. in B73 Convert sites to meme file module load meme/4.12.0-py2-openmpi3-qbrm5zn iupac2meme TTWCCATATW > batista_motif1_meme.txt iupac2meme CCAWAAATGG > batista_motif2_meme.txt cat batista_motif1_meme.txt batista_motif2_meme.txt > batista_motifs_meme.txt module load bedtools2/2.27.1-s2mtpsu cut -f 1,4,5,9 B73.structuralTEv2.2018.12.20.filteredTE.disjoined.gff3 | bedtools getfasta -name -fi Zea_mays.AGPv4.dna.toplevel.fa -bed - -fo B73v4.disjoinedTE.fa fimo batista_motifs_meme.txt B73v4.disjoinedTE.fa > fimo.txt cp fimo_out/fimo.txt batista_motifs_fimo_out.txt
grep gene Zea_mays.AGPv4.33.gff3 | cut -f 1,4,5,9 - | bedtools getfasta -name -fi Zea_mays.AGPv4.dna.toplevel.fa -bed - -fo B73v4.gene.fa fimo batista_motifs_meme.txt B73v4.gene.fa cp fimo_out/fimo.txt batista_motifs_fimo_gene_out.txt
all.sites <- read.table("batista_motifs_fimo_out.txt",header=F,stringsAsFactors = F,sep="\t")
all.sites$te <- substr(all.sites$V3,4,24)
sites.per.te <- data.frame(all.sites %>% group_by(te) %>% dplyr::summarise(motif1 = sum(V1 == "TTWCCATATW"),motif2 = sum(V1 == "CCAWAAATGG")))
sites.per.te$motif.type <- ifelse(sites.per.te$motif1 > 0 & sites.per.te$motif2 > 0, "both",
ifelse(sites.per.te$motif1 > 0, "motif1","motif2"))
names(sites.per.te)[1] <- "feature"
sites.per.te$feature_type <- "TE"Gene binding sites
gene.sites <- read.table("batista_motifs_fimo_gene_out.txt",header=F,stringsAsFactors = F,sep="\t")
gene.sites$gene <- substr(gene.sites$V3,4,17)
gene.sites2 <- subset(gene.sites,!grepl("GRMZM",gene.sites$gene))
sites.per.gene <- data.frame(gene.sites2 %>% group_by(gene) %>% dplyr::summarise(motif1 = sum(V1 == "TTWCCATATW"),motif2 = sum(V1 == "CCAWAAATGG")))
sites.per.gene$motif.type <- ifelse(sites.per.gene$motif1 > 0 & sites.per.gene$motif2 > 0, "both",
ifelse(sites.per.gene$motif1 > 0, "motif1","motif2"))
names(sites.per.gene)[1] <- "feature"
sites.per.gene$feature_type <- "gene"sites.per.feature <- data.frame(rbind(sites.per.gene,sites.per.te))
#write.table(sites.per.feature,file="PHE1_binding_sites_genesTEs.txt",quote=F,sep="\t",row.names = F)sites.per.feature <- read.table("PHE1_binding_sites_genesTEs.txt",sep = "\t",stringsAsFactors = F,header=T)b.both.contrasts$motif1 <- b.both.contrasts$feature %in% subset(sites.per.feature,sites.per.feature$motif1 > 0)[,"feature"]
b.both.contrasts$motif2 <- b.both.contrasts$feature %in% subset(sites.per.feature,sites.per.feature$motif2 > 0)[,"feature"]
b.both.contrasts$motif.type <- ifelse(b.both.contrasts$motif1 & b.both.contrasts$motif2, "both",
ifelse(b.both.contrasts$motif1, "motif1",ifelse(b.both.contrasts$motif2,"motif2","no.motif")))
table(b.both.contrasts$motif.type)
both motif1 motif2 no.motif
3235 3824 3573 10116 sites.summary <- data.frame(b.both.contrasts %>% group_by(imprint,motif.type) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))
ggplot() + geom_bar(aes(x=imprint,y=n,fill=motif.type),data=sites.summary,stat="identity",position = position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette = "YlGnBu")
ggplot() + geom_bar(aes(x=imprint,y=freq,fill=motif.type),data=sites.summary,stat="identity",position = position_dodge()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette = "YlGnBu")
b.both.contrasts$either.motif <- ifelse(b.both.contrasts$motif.type == "no.motif","no.motif","has.motif")
sites.summary2 <- data.frame(b.both.contrasts %>% group_by(imprint,either.motif) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))
sites.summary2 imprint either.motif n freq
1 matTE has.motif 73 0.5034483
2 matTE no.motif 72 0.4965517
3 MEG has.motif 96 0.4752475
4 MEG no.motif 106 0.5247525
5 no.imprint has.motif 10394 0.5122721
6 no.imprint no.motif 9896 0.4877279
7 PEG has.motif 69 0.6216216
8 PEG no.motif 42 0.3783784Look at per TE fam
te.both.contrasts <- subset(b.both.contrasts,b.both.contrasts$imprint == "matTE")
te.both.contrasts$fam <- substr(te.both.contrasts$feature,0,8)
tmp <- data.frame(table(te.both.contrasts$fam))
tmp2 <- subset(tmp,tmp$Freq>1)
site.te.summary <- data.frame(subset(te.both.contrasts,te.both.contrasts$fam %in% tmp2$Var1) %>% group_by(motif.type,fam) %>% dplyr::summarise(features = n()))
ggplot() + geom_bar(aes(x=fam,y=features,fill=motif.type),data=site.te.summary,stat="identity",position = position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) 
DHH2
DHH2.both.contrasts <- subset(b.both.contrasts,grepl("DHH00002",b.both.contrasts$feature))
DHH2.not.expressed <- subset(imp.all,grepl("DHH00002Zm00001d",imp.all$ID) & !imp.all$ID %in% b.both.contrasts$feature)
DHH2.not.expressed2 <- data.frame(matrix(ncol=ncol(DHH2.both.contrasts),nrow=nrow(DHH2.not.expressed)))
colnames(DHH2.not.expressed2) <- colnames(DHH2.both.contrasts)
DHH2.not.expressed2$feature <- DHH2.not.expressed$ID
DHH2.not.expressed2$imprint <- "not.detected"
DHH2.both.contrasts2 <- rbind(DHH2.both.contrasts,DHH2.not.expressed2)
DHH2.both.sites <- merge(DHH2.both.contrasts2,sites.per.feature,by="feature",all.x=T)
names(DHH2.both.sites) <- gsub(".y",".sites",names(DHH2.both.sites),fixed = T)
DHH2.both.sites$motif1.sites[is.na(DHH2.both.sites$motif1.sites)] <- 0
DHH2.both.sites$motif2.sites[is.na(DHH2.both.sites$motif2.sites)] <- 0
DHH2.both.sites$motif.type <- ifelse(DHH2.both.sites$motif1.sites > 0 & DHH2.both.sites$motif2.sites > 0, "both",
ifelse(DHH2.both.sites$motif1.sites > 0, "motif1",ifelse(DHH2.both.sites$motif2.sites > 0,"motif2","no.motif")))
site.dhh.summary <- data.frame(DHH2.both.sites %>% group_by(motif.type,imprint) %>% dplyr::summarise(features = n()))
ggplot() + geom_bar(aes(x=imprint,y=features,fill=motif.type),data=site.dhh.summary,stat="identity",position = position_fill()) + theme_classic() + theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette = "YlGnBu")
ggplot(DHH2.both.sites,aes(x=imprint,y=motif1.sites,fill=imprint)) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") 
ggplot(DHH2.both.sites,aes(x=imprint,y=motif2.sites,fill=imprint)) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") 
b.both.contrasts$imprint.syntelog <- paste(b.both.contrasts$imprint,b.both.contrasts$syntelog,sep="_")
gene.key.syntenic.grasses <- subset(gene.key,nchar(gene.key$sorghum_ortholog) > 0 & nchar(gene.key$foxtail_millet_ortholog) > 0 & nchar(gene.key$rice_ortholog) > 0 & nchar(gene.key$brachypodium_ortholog) > 0)
b.both.contrasts$syntenic.grasses <- ifelse(b.both.contrasts$feature_type == "TE","NA.TE",ifelse(b.both.contrasts$feature %in% gene.key.syntenic.grasses$v4_gene_model, "syntenic.grasses","nonsyntenic.grasses"))
table(b.both.contrasts[,c("imprint","syntenic.grasses","syntelog")]), , syntelog = conserved.maize
syntenic.grasses
imprint NA.TE nonsyntenic.grasses syntenic.grasses
matTE 0 0 0
MEG 0 30 24
no.imprint 0 4229 9490
PEG 0 35 52
, , syntelog = variable
syntenic.grasses
imprint NA.TE nonsyntenic.grasses syntenic.grasses
matTE 145 0 0
MEG 0 134 14
no.imprint 2712 2528 1331
PEG 0 21 3summarize.synteny1 <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint,syntelog) %>% dplyr::summarize(n=n()) %>% dplyr::mutate(freq=n/sum(n)))
a <- ggplot() + geom_bar(aes(x=imprint,y=freq,fill=syntelog),data=subset(summarize.synteny1,summarize.synteny1$syntelog == "PAV"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("green")) + theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Nonsyntenic within maize")+ theme(legend.position = "none") + ylim(c(0,1))
summarize.synteny2 <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarize(n=n()) %>% dplyr::mutate(freq=n/sum(n)))
b <- ggplot() + geom_bar(aes(x=imprint,y=freq,fill=syntenic.grasses),data=subset(summarize.synteny2,summarize.synteny2$syntenic.grasses == "nonsyntenic.grasses"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("forestgreen")) + theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Nonsyntenic within grasses")+ theme(legend.position = "none") + ylim(c(0,1))
grid.arrange(a,b,nrow=1)
How many imprinted genes have GO terms? File downloaded from AGRIGO using link: http://systemsbiology.cau.edu.cn/agriGOv2/download/958_slimGO on 20 Feb 2020
gene.go <- read.table("B73v4_slimGO.txt",header=F,sep="\t",stringsAsFactors = F)
gene.go <- gene.go[,3:4]
names(gene.go) <- c("gene","term")b.both.contrasts$has.go.term <- factor(ifelse(b.both.contrasts$feature_type == "TE", "NA.TE",
ifelse(b.both.contrasts$feature %in% gene.go$gene, "GO.term(s)","no.GO")),levels=c("GO.term(s)","no.GO"))
summarize.go <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint.syntelog,has.go.term,) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))
ggplot() + geom_bar(aes(x=imprint.syntelog,y=freq,fill=has.go.term),data=subset(summarize.go,summarize.go$has.go.term == "no.GO"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("purple")) + theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Genes without GO term")+ theme(legend.position = "none") 
b.both.contrasts.genes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "gene")
b.both.alluv <- data.frame(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint != "no.imprint") %>% group_by(imprint,syntelog,syntenic.grasses,has.go.term) %>% dplyr::summarise(n=n()))
ggplot(data=b.both.alluv,
aes(axis4 = imprint,axis3=syntelog,axis2=syntenic.grasses,axis1=has.go.term,y=n)) +
scale_x_discrete(limits = rev(c("imprint","syntelog", "syntenic.grasses", "has.go.term")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = imprint)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
theme_classic() + scale_fill_manual(values=c("magenta3","navy")) 
b.both.alluv2 <- data.frame(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint != "no.imprint") %>% group_by(imprint,syntelog,has.go.term) %>% dplyr::summarise(n=n()))
ggplot(data=b.both.alluv2,
aes(axis3 = imprint,axis2=syntelog,axis1=has.go.term,y=n)) +
scale_x_discrete(limits = rev(c("imprint","syntelog", "has.go.term")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = imprint)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
theme_classic() + scale_fill_manual(values=c("magenta3","navy")) 
b.both.alluv2b <- data.frame(b.both.contrasts.genes %>% group_by(imprint,syntenic.grasses,has.go.term,expression.type) %>% dplyr::summarise(n=n()))
b.both.alluv2b$syntenic.grasses <- factor(b.both.alluv2b$syntenic.grasses,levels=c("nonsyntenic.grasses","syntenic.grasses"))
b.both.alluv2b$has.go.term <- factor(b.both.alluv2b$has.go.term,levels=c("no.GO","GO.term(s)"))
b.both.alluv2b$expression.type <- factor(b.both.alluv2b$expression.type,levels=c("endo.preferred","constitutive"))
ggplot(data=b.both.alluv2b,
aes(axis4 = imprint,axis3=syntenic.grasses,axis2=has.go.term,axis1=expression.type,y=n)) +
scale_x_discrete(limits = rev(c("imprint","syntenic.grasses", "has.go.term","expression.type")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = syntenic.grasses)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
theme_classic() + scale_fill_manual(values=c("green","steelblue")) + facet_grid(.~imprint,scales="free")
Stats for above - chisquared test
m.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("MEG","no.imprint"))
a <- data.frame(m.stats %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntenic.grasses, values_from = n))
a$percent <- a$syntenic.grasses/rowSums(a[,2:3])
chisq.test(a[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: a[, 2:3]
X-squared = 151.71, df = 1, p-value < 2.2e-16b <- data.frame(m.stats %>% group_by(imprint,has.go.term) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = has.go.term, values_from = n))
b$percent <- b$no.GO/rowSums(b[,2:3])
chisq.test(b[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: b[, 2:3]
X-squared = 97.181, df = 1, p-value < 2.2e-16c <- data.frame(m.stats %>% group_by(imprint,expression.type) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = expression.type, values_from = n))
c$percent <- c$endo.preferred/rowSums(c[,2:3])
chisq.test(c[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: c[, 2:3]
X-squared = 2681.2, df = 1, p-value < 2.2e-16p.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("PEG","no.imprint"))
d <- data.frame(p.stats %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntenic.grasses, values_from = n))
d$percent <- d$syntenic.grasses/rowSums(d[,2:3])
chisq.test(d[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: d[, 2:3]
X-squared = 6.2213, df = 1, p-value = 0.01262e <- data.frame(p.stats %>% group_by(imprint,has.go.term) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = has.go.term, values_from = n))
e$percent <- e$no.GO/rowSums(e[,2:3])
chisq.test(e[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: e[, 2:3]
X-squared = 2.4303, df = 1, p-value = 0.119f <- data.frame(p.stats %>% group_by(imprint,expression.type) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = expression.type, values_from = n))
f$percent <- f$endo.preferred/rowSums(f[,2:3])
chisq.test(f[,2:3])Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: f[, 2:3]
X-squared = 263.19, df = 1, p-value < 2.2e-16Version 2
b.both.alluv2b <- data.frame(b.both.contrasts.genes %>% group_by(imprint,syntelog,syntenic.grasses,expression.type) %>% dplyr::summarise(n=n()))
b.both.alluv2b$syntenic.grasses <- factor(b.both.alluv2b$syntenic.grasses,levels=c("nonsyntenic.grasses","syntenic.grasses"))
b.both.alluv2b$syntelog <- factor(b.both.alluv2b$syntelog,levels=c("variable","conserved.maize"))
b.both.alluv2b$expression.type <- factor(b.both.alluv2b$expression.type,levels=c("endo.preferred","constitutive"))
ggplot(data=b.both.alluv2b,
aes(axis4 = imprint,axis3 = syntelog,axis2=syntenic.grasses,axis1=expression.type,y=n)) +
scale_x_discrete(limits = rev(c("imprint","syntelog","syntenic.grasses","expression.type")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = syntelog)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
theme_classic() + scale_fill_manual(values=c("green","steelblue")) + facet_grid(.~imprint,scales="free")
m.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("MEG","no.imprint"))
p.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("PEG","no.imprint"))
a <- data.frame(m.stats %>% group_by(imprint,syntelog) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntelog, values_from = n))
chisq.test(a[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: a[, 2:3]
X-squared = 298.28, df = 1, p-value < 2.2e-16d <- data.frame(p.stats %>% group_by(imprint,syntelog) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntelog, values_from = n))
chisq.test(d[,2:3])
Pearson's Chi-squared test with Yates' continuity correction
data: d[, 2:3]
X-squared = 3.0734e-26, df = 1, p-value = 1head(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "MEG" & b.both.contrasts.genes$syntenic.grasses == "nonsyntenic.grasses" & b.both.contrasts.genes$has.go.term == "no.GO")) feature filter.imprint.BW syntelog.BW imprinted.both.BW filter.imprint.BP syntelog.BP feature_type
2971 Zm00001d001971 MEG non.syntelog non.syntelog no.imprint non.syntelog gene
3366 Zm00001d002991 MEG non.syntelog non.syntelog MEG non.syntelog gene
3427 Zm00001d003158 MEG non.syntelog non.syntelog MEG non.syntelog gene
3730 Zm00001d004147 MEG non.syntelog non.syntelog MEG non.syntelog gene
3769 Zm00001d004412 MEG non.syntelog non.syntelog not.detected NA gene
4088 Zm00001d005617 no.imprint non.syntelog non.syntelog MEG non.syntelog gene
imprint syntelog agreement expression.type motif1 motif2 motif.type either.motif imprint.syntelog
2971 MEG variable mismatched_call endo.preferred FALSE FALSE no.motif no.motif MEG_variable
3366 MEG variable matched.call endo.preferred TRUE TRUE both has.motif MEG_variable
3427 MEG variable matched.call endo.preferred FALSE FALSE no.motif no.motif MEG_variable
3730 MEG variable matched.call endo.preferred TRUE TRUE both has.motif MEG_variable
3769 MEG variable mismatched_call endo.preferred FALSE FALSE no.motif no.motif MEG_variable
4088 MEG variable mismatched_call endo.preferred FALSE FALSE no.motif no.motif MEG_variable
syntenic.grasses has.go.term
2971 nonsyntenic.grasses no.GO
3366 nonsyntenic.grasses no.GO
3427 nonsyntenic.grasses no.GO
3730 nonsyntenic.grasses no.GO
3769 nonsyntenic.grasses no.GO
4088 nonsyntenic.grasses no.GOmatTE things.
b.both.te <- subset(b.both.contrasts,b.both.contrasts$feature_type == "TE")
shared.BWP <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation == 21) & te.anno.compare$W22 == "present" & nchar(te.anno.compare$W22.annotation == 21)& te.anno.compare$PH207 == "present" & nchar(te.anno.compare$PH207.annotation == 21))
b.both.te$shared.TE.BW <- ifelse(b.both.te$feature %in% shared.BW$B73.annotation, "shared.in.BW","variable.BW")
b.both.te$shared.TE.BP <- ifelse(b.both.te$feature %in% shared.bp$B73.annotation, "shared.in.BP","variable.BP")
b.both.te$shared.TE <- ifelse(b.both.te$feature %in% shared.BWP$B73.annotation, "shared.in.all","variable")
mat.tes <- subset(b.both.te,b.both.te$imprint == "matTE")mat.tes$order <- substr(mat.tes$feature,0,2)
table(mat.tes[,c("order","shared.TE")]) shared.TE
order shared.in.all variable
DH 4 52
DT 0 9
RI 0 2
RL 6 72mat.tes$fam <- substr(mat.tes$feature,0,8)
tmp <- data.frame(table(mat.tes$fam))
nrow(tmp)[1] 84subset(tmp,tmp$Freq > 1) Var1 Freq
1 DHH00001 3
2 DHH00002 44
5 DHH00015 3
21 RLC00002 5
22 RLC00004 3
34 RLG00001 4
35 RLG00003 4
36 RLG00005 3b.both.tes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "TE")
b.both.tes$order <- factor(substr(b.both.tes$feature,0,2),levels=c("DH","DT","RL","RI","RS"))
b.both.tes$shared.TE <- ifelse(b.both.tes$feature %in% shared.BWP$B73.annotation, "shared.in.all","variable")
mat.tes.alluv <- data.frame(subset(b.both.tes,b.both.tes$order %in% c("RL","DT","DH")) %>% group_by(imprint,order,shared.TE,expression.type) %>% dplyr::summarise(n = n()))
ggplot(data=mat.tes.alluv,
aes(axis4 = imprint, axis3 = order,axis2=shared.TE,axis1=expression.type,y=n)) +
scale_x_discrete(limits = rev(c("imprint","order","shared.TE", "expression.type")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = order)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
theme_classic() + facet_grid(.~imprint,scales="free") + scale_fill_manual(values=c("steelblue","green","darkorchid3"))
mat.tes.alluv <- data.frame(b.both.tes %>% group_by(imprint,order,shared.TE,expression.type) %>% dplyr::summarise(n = n()))
ggplot(data=subset(mat.tes.alluv,mat.tes.alluv$imprint == "matTE"),
aes(axis3 = order,axis2=shared.TE,axis1=expression.type,y=n)) +
scale_x_discrete(limits = rev(c("order","shared.TE", "expression.type")), expand = c(.1, .05)) +
xlab("features") +
geom_alluvium(aes(fill = order)) +
geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) +
theme_classic() + scale_fill_manual(values=c("steelblue","green","darkorchid3","hotpink")) + coord_flip()
B73.te.attributes <- read.table("B73_filteredTE_combined_attributes_12.20.18.txt",header=T,stringsAsFactors = F,sep="\t")
B73.te.attributes$imprint <- ifelse(B73.te.attributes$TE %in% mat.tes$feature, "matTE",
ifelse(B73.te.attributes$TE %in% b.both.contrasts$feature, "no.imprint","not.detected"))
LTR.age <- subset(B73.te.attributes,!is.na(B73.te.attributes$LTRsimilarity))
ggplot(LTR.age,aes(x=imprint,y=LTRsimilarity,fill=imprint))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") 
ggplot(subset(B73.te.attributes,B73.te.attributes$order %in% c("RL","DT","DH")),aes(x=imprint,y=disjoined,fill=imprint))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") + facet_wrap(.~order,scales = "free")
Tissue-specific expression
table(b.both.te[,c("imprint","expression.type")]) expression.type
imprint constitutive endo.preferred
matTE 12 133
no.imprint 1247 1465133/(133 + 12)[1] 0.91724141465/(1465 + 1247)[1] 0.5401917are maternal TEs enriched for their closest gene being a MEG?
# write.table(meg.tes[,1,drop=F],file="matTEs.txt",quote=F,row.names = F,col.names = F)Find closest gene to matTEs
module load bedtools2/2.27.1-s2mtpsu sort -k 1,1 -k4,4n matTEs.gff3 | bedtools closest -D b -t all -wa -wb -a - -b /work/LAS/sna-lab/sna/B74v4/Zea_mays.AGPv4.33.gene.gff3 > matTE_closest_gene_v2.bed cut -f 9,18,19 matTE_closest_gene_v2.bed > matTE_closest_gene_v2.txt
matTE_closest_gene <- read.table("matTE_closest_gene_v2.txt",header=F,stringsAsFactors = F,sep="\t")
matTE_closest_gene$TE <- substr(matTE_closest_gene$V1,4,24)
matTE_closest_gene$gene <- substr(matTE_closest_gene$V2,4,17)
matTE_closest_gene$distance <- matTE_closest_gene$V3
matTE_closest_gene$TE.type <- ifelse(matTE_closest_gene$TE %in% meg.tes$feature,"matTE","No.Imprint")
matTE_closest_gene$gene.type <- ifelse(matTE_closest_gene$gene %in% meg.genes$feature,"MEG",
ifelse(matTE_closest_gene$gene %in% peg.genes$feature, "PEG","No.Imprint"))
matTE_closest_gene$distance.cat <- ifelse(matTE_closest_gene$distance == 0, "overlap",
ifelse(abs(matTE_closest_gene$distance) < 5000, "near","far"))
table(matTE_closest_gene[,c("gene.type","distance.cat")]) distance.cat
gene.type far near overlap
MEG 3 9 7
No.Imprint 47 43 45table(matTE_closest_gene[,c("gene.type","TE.type")]) TE.type
gene.type matTE
MEG 19
No.Imprint 135Binomial test - are matTEs enriched for having MEG as nearest gene?
successes <- nrow(subset(matTE_closest_gene,matTE_closest_gene$gene.type == "MEG"))
trials <- length(unique(matTE_closest_gene$TE))
proportion.genes.MEG <- nrow(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "MEG"))/nrow(b.both.contrasts.genes)
binom.test(successes,trials,proportion.genes.MEG,alternative = "greater")
Exact binomial test
data: successes and trials
number of successes = 19, number of trials = 145, p-value = 7.211e-15
alternative hypothesis: true probability of success is greater than 0.01129059
95 percent confidence interval:
0.08753504 1.00000000
sample estimates:
probability of success
0.1310345 successes.peg <- 0
proportion.genes.PEG <- nrow(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "PEG"))/nrow(b.both.contrasts.genes)
binom.test(successes.peg,trials,proportion.genes.PEG,alternative = "less")
Exact binomial test
data: successes.peg and trials
number of successes = 0, number of trials = 145, p-value = 0.4056
alternative hypothesis: true probability of success is less than 0.006204237
95 percent confidence interval:
0.00000000 0.02044826
sample estimates:
probability of success
0 plot.test <- data.frame(matrix(NA,nrow=4,ncol=3))
names(plot.test) <- c("set","imprint","freq")
plot.test$imprint <- c("MEG","MEG","PEG","PEG")
plot.test$set <- c("MEG.success","proportion.MEG","PEG.success","proportion.PEG")
plot.test$freq <- c(successes/trials,proportion.genes.MEG,0,proportion.genes.PEG)
ggplot() + geom_bar(aes(x=set,y=freq,fill="red"),data=plot.test,stat="identity",position = "identity", show.legend = FALSE) + theme_minimal() + ylim(0,0.15) + theme(axis.text.x = element_text(angle=90, hjust=1),panel.grid.major.x = element_blank()) + facet_wrap(imprint~.,scales="free_x")
Make version with Ns and where upstream and downstream are shown
matTE.MEG <- subset(matTE_closest_gene,matTE_closest_gene$gene.type == "MEG")[,c("TE","gene","distance","TE.type","gene.type")]
plot.test2 <- plot.test[2:4,1:2]
plot.test2$count <- c(proportion.genes.MEG * trials,0,proportion.genes.PEG * trials)
plot.test2$group <- c("expected","PEG","expected")
plot.test2[4,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance < 0)),"upstream")
plot.test2[5,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance == 0)),"overlap")
plot.test2[6,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance > 0)),"downstream")
plot.test2$count <- as.numeric(plot.test2$count)
plot.test2$group <- factor(plot.test2$group,levels=c("upstream","overlap","downstream","expected","PEG"))
plot.test2$set <- factor(c("MEG.expected","PEG.nearest","PEG.expected","MEG.nearest","MEG.nearest","MEG.nearest"),levels=c("MEG.nearest","MEG.expected","PEG.nearest","PEG.expected"))
ggplot() + geom_bar(aes(x=set,y=count,fill=group),color="black",data=plot.test2,stat="identity",position = "identity") + theme_minimal() + theme(axis.text.x = element_text(angle=90, hjust=1),panel.grid.major.x = element_blank()) + facet_wrap(imprint~.,scales="free_x") + scale_fill_manual(values=c("indianred1","honeydew","lightblue3","black","yellow")) + scale_y_continuous(breaks=c(0,2,4,6,8,10),limits = c(0,11)) 
Table of matTE nearest gene is MEG
matTE.MEG$order <- substr(matTE.MEG$TE,0,3)
for(i in 1:nrow(matTE.MEG)){
if(matTE.MEG[i,"gene"] %in% plot.out2.bw$feature){
matTE.MEG[i,"ratio.BW.gene"] <- subset(plot.out2.bw,plot.out2.bw$feature == matTE.MEG[i,"gene"])[,"maternal_preference"]
}
else(matTE.MEG[i,"ratio.BW.gene"] <- "NA")
if(matTE.MEG[i,"gene"] %in% plot.out2.bp$feature){
matTE.MEG[i,"ratio.BP.gene"] <- subset(plot.out2.bp,plot.out2.bp$feature == matTE.MEG[i,"gene"])[,"maternal_preference"]
}
else(matTE.MEG[i,"ratio.BP.gene"] <- "NA")
if(matTE.MEG[i,"TE"] %in% plot.out2.bw$feature){
matTE.MEG[i,"ratio.BW.TE"] <- subset(plot.out2.bw,plot.out2.bw$feature == matTE.MEG[i,"TE"])[,"maternal_preference"]
}
else(matTE.MEG[i,"ratio.BW.TE"] <- "NA")
if(matTE.MEG[i,"TE"] %in% plot.out2.bp$feature){
matTE.MEG[i,"ratio.BP.TE"] <- subset(plot.out2.bp,plot.out2.bp$feature == matTE.MEG[i,"TE"])[,"maternal_preference"]
}
else(matTE.MEG[i,"ratio.BP.TE"] <- "NA")
matTE.MEG[i,"gene.variability"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"gene"])[,"syntelog"]
matTE.MEG[i,"gene.expression"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"gene"])[,"expression.type"]
matTE.MEG[i,"TE.expression"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"TE"])[,"expression.type"]
}
#write.table(matTE.MEG[order(matTE.MEG$distance),],file="matTE_closest_gene_is_MEG_v2.txt",quote=F,sep="\t",row.names = F)matTE.MEG2 <- merge(matTE.MEG,te.anno.compare,by="TE",all.x=T)Output files
# write.table(b.both.contrasts,file="B73_features_both_contrasts.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.bw)[14] <- "RER"
# plot.out2.bw$imprint <- NULL
# write.table(plot.out2.bw,file="B73.v.W22_features.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.bp)[14] <- "RER"
# plot.out2.bp$imprint <- NULL
# write.table(plot.out2.bp,file="B73.v.PH207_features.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.wp)[14] <- "RER"
# plot.out2.wp$imprint <- NULL
# write.table(plot.out2.wp,file="W22.v.PH207_features.out.txt",sep="\t",quote=F,row.names = F)---
title: "Imprinting Github"
output: html_notebook
---

```{r}
library(tidyr)
library(ggplot2)
library(dplyr)
library(matrixStats)
library(DESeq2)
library(stringr)
library(ggExtra)
library(gridExtra)
library(pheatmap)
library(reshape2)
library("ggalluvial")
```

```{r}
give.n <- function(x){
   return(c(y = mean(x), label = length(x)))
}
```

SNP-ASE output
See https://github.com/orionzhou/rnaseq/blob/master/output.md
```{r}
rds <- readRDS("ase_1Oct19.rds")
ase.reads <- rds
cpm <- readRDS("cpm.rds")
lib.size.tab <- data.frame(cpm$tl)
```

```{r}
ase.reads$ratio <- ase.reads$allele1/(ase.reads$allele1 + ase.reads$allele2)
ase.reads$total.reads <- (ase.reads$allele1 + ase.reads$allele2)
ase.reads$contrast <- substr(ase.reads$sid,0,2)
ase.reads2 <- data.frame(ase.reads %>% 
                           group_by(gid,contrast) %>% 
                           dplyr::summarize(
                             mean.ratio = mean(ratio),
                             mean.count = mean(total.reads)))
```

Gene key
B73 reference gene model cross-reference relative to v4
Downloaded from MaizeGDB 2020/01/22, 11:32am
Reformatted to remove header
```{r}
gene.key <- read.table("gene_model_xref_v4c.txt",sep="\t",header=T,stringsAsFactors = F)
dup.gene <- subset(gene.key,duplicated(gene.key$v4_gene_model))
gene.key <- subset(gene.key,!gene.key$v4_gene_model %in% dup.gene$v4_gene_model)
gene.key.BW.syntelogs <- subset(gene.key,nchar(gene.key$W22.Zm00004b.1.) == 14)[,c(1:4,8,10,20,24)]
gene.key.bp.syntelogs <- subset(gene.key,nchar(gene.key$PH207.Zm00008a.1.) == 14)[,c(1:4,8,10,20,24)]
gene.key.BWP <- subset(gene.key,nchar(gene.key$W22.Zm00004b.1.) == 14 & nchar(gene.key$PH207.Zm00008a.1.) == 14)[,c(1:4,8,10,20,24)]
```

Read in RER table and calculate RPM
```{r}
imp.all <- read.table("combined_counts_all_concatinated_genomes_21Oct19.txt",header=T,stringsAsFactors = F) # update path 
imp.all$ID <- str_replace_all(imp.all$ID,".v1.1","")
imp.all.rpm <- imp.all
for(i in 2:19){
  imp.all.rpm[,i] <- imp.all[,i]/lib.size.tab[lib.size.tab$SampleID == colnames(imp.all[i]),"libSize"] * 1e6
}

imp.all2 <- subset(imp.all,!imp.all$ID %in% c("no_feature","ambiguous","too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
imp.all2$ID[grepl("gene",imp.all2$ID)] <- substr(imp.all2$ID[grepl("gene",imp.all2$ID)],6,19) # fix formatting
```

Summary stats about libraries
```{r}
imp.all.numerator <- subset(imp.all,!imp.all$ID %in% c("too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
imp.all.denominator <- subset(imp.all,!imp.all$ID %in% c("too_low_aQual","not_aligned")) # remove non-feature and ambiguous reads

summary.stats.libraries <- data.frame(cbind(colSums(imp.all.denominator[,2:19]),(colSums(imp.all.numerator[,2:19])/colSums(imp.all.denominator[,2:19]))*100))
names(summary.stats.libraries) <- c("Reads","Percent_Unique")
mean(summary.stats.libraries$Percent_Unique)
```


```{r}
imp.all.rpm2 <- subset(imp.all.rpm,!imp.all.rpm$ID %in% c("no_feature","ambiguous","too_low_aQual","not_aligned","alignment_not_unique")) # remove non-feature and ambiguous reads
rownames(imp.all.rpm2) <- imp.all.rpm2$ID
imp.all.rpm2$ID <- NULL
rownames(imp.all.rpm2)[grepl("gene",rownames(imp.all.rpm2))] <- substr(rownames(imp.all.rpm2)[grepl("gene",rownames(imp.all.rpm2))],6,19) # fix formatting
```


```{r}
imp.all.rpm2$BW.mean <- rowMeans(imp.all.rpm2[,1:3]) #ignore this (I hard coded column numbers later)
imp.all.rpm2$WB.mean <- rowMeans(imp.all.rpm2[,4:6]) #ignore this (I hard coded column numbers later)
imp.all.rpm2$B_ratio <- imp.all.rpm2$BW.mean/(imp.all.rpm2$BW.mean + imp.all.rpm2$WB.mean) #ignore this (I hard coded column numbers later)

imp.all.rpm2$feature_type <- ifelse(nchar(rownames(imp.all.rpm2)) == 21, "TE","gene") # Label features as TEs or genes
imp.all.rpm2$genome <- ifelse(grepl("Zm00001d",rownames(imp.all.rpm2)),"B73",ifelse(grepl("Zm00004b",rownames(imp.all.rpm2)),"W22","PH207")) # Label which genome the feature annotation is from
table(imp.all.rpm2$genome)
```

For each pair of genotypes, make the same columns and then do the math for the maternal preference ratio
```{r}
imp.BvW.rpm <- imp.all.rpm2[,c(1:6,22:23)]
imp.BvW.rpm$contrast <- "BW"
imp.BvW.rpm$type <- ifelse(imp.BvW.rpm$genome == "B73","A","B")
imp.BvW.rpm$feature <- rownames(imp.BvW.rpm)

imp.BvP.rpm <- imp.all.rpm2[,c(7:12,22:23)]
imp.BvP.rpm$contrast <- "BP"
imp.BvP.rpm$type <- ifelse(imp.BvW.rpm$genome == "B73","A","B")
imp.BvP.rpm$feature <- rownames(imp.BvP.rpm)

imp.WvP.rpm <- imp.all.rpm2[,c(13:18,22:23)]
imp.WvP.rpm$contrast <- "WP"
imp.WvP.rpm$type <- ifelse(imp.BvW.rpm$genome == "W22","A","B")
imp.WvP.rpm$feature <- rownames(imp.WvP.rpm)

names(imp.BvW.rpm) <- names(imp.BvP.rpm) <- names(imp.WvP.rpm) <- c("AB1","AB2","AB3","BA1","BA2","BA3","feature_type","genome","contrast","type","feature")

imp.recips.rpm <- data.frame(rbind(imp.BvW.rpm,imp.BvP.rpm,imp.WvP.rpm))

imp.recips.rpm$A_mean <- rowMeans(imp.recips.rpm[,1:3])
imp.recips.rpm$B_mean <- rowMeans(imp.recips.rpm[,4:6])
imp.recips.rpm$maternal_preference <- ifelse(imp.recips.rpm$type == "A",imp.recips.rpm$A_mean/(imp.recips.rpm$A_mean + imp.recips.rpm$B_mean),imp.recips.rpm$B_mean/(imp.recips.rpm$A_mean + imp.recips.rpm$B_mean)) # maternal preferance calculation
```

Plot the maternal preferance distributipns for each contrast, genome, and feature type
```{r}
imp.recips.rpm.filter <- subset(imp.recips.rpm,imp.recips.rpm$A_mean >= 1 | imp.recips.rpm$B_mean >= 1)

ggplot(imp.recips.rpm.filter,aes(x=genome,y=maternal_preference,fill=genome))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_type~contrast,scales="free_x")  + stat_summary(fun.data = give.n, geom = "text") 
```

Look at per-element or gene expression across development
```{r}
dev.reads <- read.table("element_combined_counts_DevREDO_11Oct19.txt",header=T,stringsAsFactors = F,sep="\t")
```

```{r}
rownames(dev.reads) <- dev.reads$element
dev.reads$element <- NULL
dev.reads2 <- dev.reads[,grep("te.g",colnames(dev.reads),invert = T)]
names(dev.reads2) <- substr(names(dev.reads2),0,nchar(names(dev.reads2))-7)
dev.reads3 <- dev.reads2[,grep("DS",colnames(dev.reads2))]

  
dev.rpm <- dev.reads3
for(i in 1:ncol(dev.rpm)){
  dev.rpm[,i] <- dev.reads3[,i]/sum(dev.reads3[,i]) * 1e6
}
```


```{r}
all.samples <- data.frame(names(dev.rpm))
all.samples$colname <- paste(names(dev.rpm))
all.samples <- separate(all.samples,names.dev.rpm.,c("tissue","details","experiment","genotype","rep"),sep="_")
```

Make filter for potential maternal contamination MEGs (TE plus gene)
```{r}
keep.columns <- c("endosperm_14dap_DS_B_1","endosperm_14dap_DS_B_2","endosperm_14dap_DS_B_3","seed_pericarp.18dap_DS_B_1","seed_pericarp.18dap_DS_B_2","seed_pericarp.18dap_DS_B_3")
B73.endoperi.stepflug <- subset(dev.rpm, rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns]
B73.endoperi.stepflug$mean.endo  <- rowMeans(B73.endoperi.stepflug[,1:3])
B73.endoperi.stepflug$mean.peri <- rowMeans(B73.endoperi.stepflug[,4:6])
peri.preferred <- subset(B73.endoperi.stepflug,B73.endoperi.stepflug$mean.peri > 2 * B73.endoperi.stepflug$mean.endo)
nrow(peri.preferred)
table(nchar(rownames(peri.preferred)))
peri.preferred.W22 <- subset(gene.key,gene.key$v4_gene_model %in% rownames(peri.preferred))[,"W22.Zm00004b.1."]
peri.preferred.PH207 <- subset(gene.key,gene.key$v4_gene_model %in% rownames(peri.preferred))[,"PH207.Zm00008a.1."]
```

Use DEseq to call significance over a log2FC cutoff of 1 (2-fold, which is the expected ratio)
```{r}
imp.all.de.bw <- imp.all2[,2:7]
rownames(imp.all.de.bw) <- imp.all2$ID
#imp.all.de.keep.bw <- subset(imp.all.de.bw,rowSums(imp.all.de.bw >0) >= 3) 
imp.all.de.keep.bw <- subset(imp.all.de.bw,rowSums(imp.all.de.bw) >= 10) 
#imp.all.de.keep.bw <- subset(imp.all.de.bw,rowMeans(imp.all.rpm2[,1:3]) >= 0.5 | rowMeans(imp.all.rpm2[,4:6]) >= 0.5) 

sample.info.bw <- as.data.frame(cbind(paste(names(imp.all.de.keep.bw)),c("BW","BW","BW","WB","WB","WB")))
rownames(sample.info.bw) <- sample.info.bw[,1]
sample_list.bw <- sample.info.bw[,2]
names(sample.info.bw) <- c("sample.info.bw","sample_list")

dds.bw <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.bw,colData = sample.info.bw, design = ~ sample_list)
dds.bw <- DESeq(dds.bw)
res.bw <- results(dds.bw, lfcThreshold=1, altHypothesis = "greaterAbs")
drawLines <- function() abline(h=c(-1,1),col="dodgerblue",lwd=2)
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.bw, ylim=ylim); drawLines()
```

Plot results with respect to RER
```{r}
out.bw <- data.frame(res.bw)
plot.out.bw <- merge(imp.recips.rpm,out.bw,by.x="feature",by.y="row.names",all=F)
plot.out.bw$sig <- ifelse(plot.out.bw$padj < 0.05, "TRUE", "FALSE")
plot.out2.bw <- subset(plot.out.bw,!is.na(plot.out.bw$sig) & plot.out.bw$contrast %in% c("BW","WB"))
plot.out2.bw$category <- ifelse(plot.out2.bw$padj < 0.05 & plot.out2.bw$log2FoldChange < -1 & (plot.out2.bw$maternal_preference > .9 | plot.out2.bw$maternal_preference < .1), "up",ifelse(plot.out2.bw$padj < 0.05 & plot.out2.bw$log2FoldChange > 1 & (plot.out2.bw$maternal_preference > .9 | plot.out2.bw$maternal_preference < .1),"down","notDE"))

plot.out2.bw$imprint <- ifelse(plot.out2.bw$category =="up" & plot.out2.bw$genome == "B73","MEG",
                               ifelse(plot.out2.bw$category =="down" & plot.out2.bw$genome == "B73","PEG",
                                      ifelse(plot.out2.bw$category =="down" & plot.out2.bw$genome == "W22","MEG",
                                             ifelse(plot.out2.bw$category =="up" & plot.out2.bw$genome == "W22","PEG","no.imprint"))))

ggplot(plot.out2.bw,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 

plot.out2.bw$filter.imprint <- ifelse(plot.out2.bw$imprint == "MEG" & (plot.out2.bw$feature %in% rownames(peri.preferred) | plot.out2.bw$feature %in% peri.preferred.W22),"filtered.MEG",plot.out2.bw$imprint)
```



Repeat for BP
```{r}
imp.all.de.bp <- imp.all2[,8:13]
rownames(imp.all.de.bp) <- imp.all2$ID
#imp.all.de.keep.bp <- subset(imp.all.de.bp,rowSums(imp.all.de.bp >0) >= 3)
imp.all.de.keep.bp <- subset(imp.all.de.bp,rowSums(imp.all.de.bp) >= 10)
#imp.all.de.keep.bp <- subset(imp.all.de.bp,rowMeans(imp.all.rpm2[,7:9]) >= 0.5 | rowMeans(imp.all.rpm2[,10:12]) >= 0.5) 

sample.info.bp <- as.data.frame(cbind(paste(names(imp.all.de.keep.bp)),c("BP","BP","BP","PB","PB","PB")))
rownames(sample.info.bp) <- sample.info.bp[,1]
sample_list.bp <- sample.info.bp[,2]
names(sample.info.bp) <- c("sample.info.bp","sample_list")

dds.bp <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.bp,colData = sample.info.bp, design = ~ sample_list)
dds.bp <- DESeq(dds.bp)
res.bp <- results(dds.bp, lfcThreshold=1, altHypothesis = "greaterAbs")
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.bp, ylim=ylim); drawLines()
```

Plot results with respect to RER
```{r}
out.bp <- data.frame(res.bp)
plot.out.bp <- merge(imp.recips.rpm,out.bp,by.x="feature",by.y="row.names",all=F)
plot.out.bp$sig <- ifelse(plot.out.bp$padj < 0.05, "TRUE", "FALSE")
plot.out2.bp <- subset(plot.out.bp,!is.na(plot.out.bp$sig) & plot.out.bp$contrast %in% c("BP","PB"))

plot.out2.bp$category <- ifelse(plot.out2.bp$padj < 0.05 & plot.out2.bp$log2FoldChange < -1 & (plot.out2.bp$maternal_preference > .9 | plot.out2.bp$maternal_preference < .1), "up",ifelse(plot.out2.bp$padj < 0.05 & plot.out2.bp$log2FoldChange > 1 & (plot.out2.bp$maternal_preference > .9 | plot.out2.bp$maternal_preference < .1),"down","notDE"))

plot.out2.bp$imprint <- ifelse(plot.out2.bp$category =="up" & plot.out2.bp$genome == "B73","MEG",
                               ifelse(plot.out2.bp$category =="down" & plot.out2.bp$genome == "B73","PEG",
                                      ifelse(plot.out2.bp$category =="down" & plot.out2.bp$genome == "PH207","MEG",
                                             ifelse(plot.out2.bp$category =="up" & plot.out2.bp$genome == "PH207","PEG","no.imprint"))))

ggplot(plot.out2.bp,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 

plot.out2.bp$filter.imprint <- ifelse(plot.out2.bp$imprint == "MEG" & (plot.out2.bp$feature %in% rownames(peri.preferred) | plot.out2.bp$feature %in% peri.preferred.PH207),"filtered.MEG",plot.out2.bp$imprint)
```

Repeat for WP
```{r}
imp.all.de.wp <- imp.all2[,14:19]
rownames(imp.all.de.wp) <- imp.all2$ID
#imp.all.de.keep.wp <- subset(imp.all.de.wp,rowSums(imp.all.de.wp >0) >= 3)
imp.all.de.keep.wp <- subset(imp.all.de.wp,rowSums(imp.all.de.wp) >= 10)
#imp.all.de.keep.wp <- subset(imp.all.de.wp,rowMeans(imp.all.rpm2[,13:15]) >= 0.5 | rowMeans(imp.all.rpm2[,16:18]) >= 0.5) 

sample.info.wp <- as.data.frame(cbind(paste(names(imp.all.de.keep.wp)),c("WP","WP","WP","PW","PW","PW")))
rownames(sample.info.wp) <- sample.info.wp[,1]
sample_list.wp <- sample.info.wp[,2]
names(sample.info.wp) <- c("sample.info.wp","sample_list")

dds.wp <- DESeqDataSetFromMatrix(countData = imp.all.de.keep.wp,colData = sample.info.wp, design = ~ sample_list)
dds.wp <- DESeq(dds.wp)
res.wp <- results(dds.wp, lfcThreshold=1, altHypothesis = "greaterAbs")
drawLines <- function() abline(h=c(-1,1),col="dodgerblue",lwd=2)
#par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
plotMA(res.wp, ylim=ylim); drawLines()
```

Plot results with respect to the RER
```{r}
out.wp <- data.frame(res.wp)
plot.out.wp <- merge(imp.recips.rpm,out.wp,by.x="feature",by.y="row.names",all=F)
plot.out.wp$sig <- ifelse(plot.out.wp$padj < 0.05, "TRUE", "FALSE")
plot.out2.wp <- subset(plot.out.wp,!is.na(plot.out.wp$sig) & plot.out.wp$contrast %in% c("WP","PW"))
plot.out2.wp$category <- ifelse(plot.out2.wp$padj < 0.05 & plot.out2.wp$log2FoldChange < -1 & (plot.out2.wp$maternal_preference > .9 | plot.out2.wp$maternal_preference < .1), "up",ifelse(plot.out2.wp$padj < 0.05 & plot.out2.wp$log2FoldChange > 1 & (plot.out2.wp$maternal_preference > .9 | plot.out2.wp$maternal_preference < .1),"down","notDE"))

plot.out2.wp$imprint <- ifelse(plot.out2.wp$category =="up" & plot.out2.wp$genome == "PH207","MEG",
                               ifelse(plot.out2.wp$category =="down" & plot.out2.wp$genome == "PH207","PEG",
                                      ifelse(plot.out2.wp$category =="down" & plot.out2.wp$genome == "W22","MEG",
                                             ifelse(plot.out2.wp$category =="up" & plot.out2.wp$genome == "W22","PEG","no.imprint"))))


ggplot(plot.out2.wp,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~feature_type) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 

plot.out2.wp$filter.imprint <- ifelse(plot.out2.wp$imprint == "MEG" & (plot.out2.wp$feature %in% peri.preferred.W22 | plot.out2.wp$feature %in% peri.preferred.PH207),"filtered.MEG",plot.out2.wp$imprint)
```

Summarize imprinting calls
```{r}
summary.bw <- data.frame(plot.out2.bw %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))
summary.bp <- data.frame(plot.out2.bp %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))
summary.wp <- data.frame(plot.out2.wp %>% group_by(genome,feature_type,filter.imprint,contrast) %>% summarize(count = n()))

summary.combined <- rbind(summary.bw,summary.bp,summary.wp)
summary.combined

summary.combined2 <- subset(summary.combined,summary.combined$filter.imprint %in% c("MEG","PEG"))
summary.combined2[summary.combined2$filter.imprint == "PEG","count"] <- -summary.combined2[summary.combined2$filter.imprint == "PEG","count"]
summary.combined2$label <- paste(summary.combined2$genome,summary.combined2$contrast,sep="_")

ggplot() + geom_bar(aes(x=label,y=count,fill=filter.imprint),data=summary.combined2,stat="identity",position = "identity") + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + facet_grid(.~feature_type,scales="free") + scale_fill_manual(values=c("magenta2","navy")) 
```
Average imprinted genes
```{r}
imp.genes <- subset(summary.combined2,summary.combined2$feature_type == "gene")
imp.genes[imp.genes$filter.imprint == "PEG","count"] <- -imp.genes[imp.genes$filter.imprint == "PEG","count"]

imp.genes2 <- imp.genes%>% group_by(label) %>% dplyr::summarise(total.imprint = sum(count))
mean(imp.genes2$total.imprint)

mean(subset(imp.genes,imp.genes$filter.imprint == "MEG")[,"count"])

mean(subset(imp.genes,imp.genes$filter.imprint == "PEG")[,"count"])
```

```{r}
mean(subset(summary.combined2,summary.combined2$feature_type == "TE" & summary.combined2$filter.imprint == "MEG")[,"count"])
```




Use to make venns - B73 vs W22
```{r}
compare.bw.syntelogs <- subset(plot.out2.bw,plot.out2.bw$feature_type == "gene")
compare.bw.syntelogs$syntelog <- ifelse(compare.bw.syntelogs$feature %in% gene.key.BW.syntelogs$v4_gene_model | compare.bw.syntelogs$feature %in% gene.key.BW.syntelogs$W22.Zm00004b.1., "syntelog","non.syntelog")

table(compare.bw.syntelogs[,c("genome","filter.imprint","syntelog")])

compare.bw.syntelogs.b73 <- merge(subset(compare.bw.syntelogs,compare.bw.syntelogs$syntelog == "syntelog" & compare.bw.syntelogs$genome == "B73"),gene.key.BW.syntelogs,by.x="feature",by.y="v4_gene_model",all.x=T)
compare.bw.syntelogs.w22 <- subset(compare.bw.syntelogs,compare.bw.syntelogs$syntelog == "syntelog" & compare.bw.syntelogs$genome == "W22")
compare.bw.syntelogs.merge <- merge(compare.bw.syntelogs.b73,compare.bw.syntelogs.w22,by.x = "W22.Zm00004b.1.",by.y="feature",all=T)

names(compare.bw.syntelogs.merge) <- gsub(".x",".B73",names(compare.bw.syntelogs.merge),fixed = T)
names(compare.bw.syntelogs.merge) <- gsub(".y",".W22",names(compare.bw.syntelogs.merge),fixed = T)

compare.bw.syntelogs.merge$imprint.B73[is.na(compare.bw.syntelogs.merge$imprint.B73)] <- "no.imprint"
compare.bw.syntelogs.merge$imprint.W22[is.na(compare.bw.syntelogs.merge$imprint.W22)] <- "no.imprint"
compare.bw.syntelogs.merge$filter.imprint.B73[is.na(compare.bw.syntelogs.merge$filter.imprint.B73)] <- "no.imprint"
compare.bw.syntelogs.merge$filter.imprint.W22[is.na(compare.bw.syntelogs.merge$filter.imprint.W22)] <- "no.imprint"

table(compare.bw.syntelogs.merge[,c("filter.imprint.B73","filter.imprint.W22")])
```

Use to make venns - B73 vs PH207
```{r}
compare.bp.syntelogs <- subset(plot.out2.bp,plot.out2.bp$feature_type == "gene")

compare.bp.syntelogs$syntelog <- ifelse(compare.bp.syntelogs$feature %in% gene.key.bp.syntelogs$v4_gene_model | compare.bp.syntelogs$feature %in% gene.key.bp.syntelogs$PH207.Zm00008a.1., "syntelog","non.syntelog")

table(compare.bp.syntelogs[,c("genome","filter.imprint","syntelog")])

compare.bp.syntelogs.b73 <- merge(subset(compare.bp.syntelogs,compare.bp.syntelogs$syntelog == "syntelog" & compare.bp.syntelogs$genome == "B73"),gene.key.bp.syntelogs,by.x="feature",by.y="v4_gene_model",all.x=T)
compare.bp.syntelogs.PH207 <- subset(compare.bp.syntelogs,compare.bp.syntelogs$syntelog == "syntelog" & compare.bp.syntelogs$genome == "PH207")
compare.bp.syntelogs.merge <- merge(compare.bp.syntelogs.b73,compare.bp.syntelogs.PH207,by.x = "PH207.Zm00008a.1.",by.y="feature",all=T)

names(compare.bp.syntelogs.merge) <- gsub(".x",".B73",names(compare.bp.syntelogs.merge),fixed = T)
names(compare.bp.syntelogs.merge) <- gsub(".y",".PH207",names(compare.bp.syntelogs.merge),fixed = T)

compare.bp.syntelogs.merge$imprint.B73[is.na(compare.bp.syntelogs.merge$imprint.B73)] <- "no.imprint"
compare.bp.syntelogs.merge$imprint.PH207[is.na(compare.bp.syntelogs.merge$imprint.PH207)] <- "no.imprint"
compare.bp.syntelogs.merge$filter.imprint.B73[is.na(compare.bp.syntelogs.merge$filter.imprint.B73)] <- "no.imprint"
compare.bp.syntelogs.merge$filter.imprint.PH207[is.na(compare.bp.syntelogs.merge$filter.imprint.PH207)] <- "no.imprint"

table(compare.bp.syntelogs.merge[,c("filter.imprint.B73","filter.imprint.PH207")])
```

matTE venns
```{r}
te.anno.compare <- read.table("non-redundant_TEs_by_annptation_4genomes_24Jan20.txt",header = T,stringsAsFactors = F)
```
B73 vs W22 TEs
```{r}
shared.BW <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation == 21) & te.anno.compare$W22 == "present" & nchar(te.anno.compare$W22.annotation == 21))

compare.bw.TE <- subset(plot.out2.bw,plot.out2.bw$feature_type == "TE" & plot.out2.bw$filter.imprint == "MEG")
compare.bw.TE$shared <- ifelse(compare.bw.TE$feature %in% shared.BW$B73.annotation | compare.bw.TE$feature %in% shared.BW$W22.annotation, "shared","variable")

table(compare.bw.TE[,c("genome","filter.imprint","shared")])

compare.bw.TE.b <- merge(subset(compare.bw.TE,compare.bw.TE$shared == "shared" & compare.bw.TE$genome == "B73"),shared.BW[,c("B73.annotation","W22.annotation")],by.x="feature",by.y="B73.annotation",all.x=T)
compare.bw.TE.w <- subset(compare.bw.TE,compare.bw.TE$shared == "shared" & compare.bw.TE$genome == "W22")
compare.bw.TE.merge <- merge(compare.bw.TE.b, compare.bw.TE.w, by.x="W22.annotation",by.y="feature",all=T)

names(compare.bw.TE.merge) <- gsub(".x",".B73",names(compare.bw.TE.merge),fixed = T)
names(compare.bw.TE.merge) <- gsub(".y",".W22",names(compare.bw.TE.merge),fixed = T)

compare.bw.TE.merge$filter.imprint.B73[is.na(compare.bw.TE.merge$filter.imprint.B73)] <- "no.imprint"
compare.bw.TE.merge$filter.imprint.W22[is.na(compare.bw.TE.merge$filter.imprint.W22)] <- "no.imprint"

table(compare.bw.TE.merge[,c("filter.imprint.B73","filter.imprint.W22")])
```


B73 vs PH207 TEs
```{r}
shared.bp <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation) == 21 & te.anno.compare$PH207 == "present" & nchar(te.anno.compare$PH207.annotation) == 21)

compare.bp.TE <- subset(plot.out2.bp,plot.out2.bp$feature_type == "TE" & plot.out2.bp$filter.imprint == "MEG")
compare.bp.TE$shared <- ifelse(compare.bp.TE$feature %in% shared.bp$B73.annotation | compare.bp.TE$feature %in% shared.bp$P207.annotation, "shared","variable")

table(compare.bp.TE[,c("genome","filter.imprint","shared")])

compare.bp.TE.b <- merge(subset(compare.bp.TE,compare.bp.TE$shared == "shared" & compare.bp.TE$genome == "B73"),shared.bp[,c("B73.annotation","PH207.annotation")],by.x="feature",by.y="B73.annotation",all.x=T)
compare.bp.TE.w <- subset(compare.bp.TE,compare.bp.TE$shared == "shared" & compare.bp.TE$genome == "PH207")
compare.bp.TE.merge <- merge(compare.bp.TE.b, compare.bp.TE.w, by.x="PH207.annotation",by.y="feature",all=T)

names(compare.bp.TE.merge) <- gsub(".x",".B73",names(compare.bp.TE.merge),fixed = T)
names(compare.bp.TE.merge) <- gsub(".y",".PH207",names(compare.bp.TE.merge),fixed = T)

compare.bp.TE.merge$filter.imprint.B73[is.na(compare.bp.TE.merge$filter.imprint.B73)] <- "no.imprint"
compare.bp.TE.merge$filter.imprint.PH207[is.na(compare.bp.TE.merge$filter.imprint.PH207)] <- "no.imprint"

table(compare.bp.TE.merge[,c("filter.imprint.B73","filter.imprint.PH207")])
```

How many MEGs called in only one direction were due to cutoffs and no data? Use B73 by W22 contrast for numbers
```{r}
B73.only.MEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "no.imprint")
B73.only.MEG.bw$category <- ifelse(is.na(B73.only.MEG.bw$maternal_preference.W22),"low.coverage",
                                   ifelse(B73.only.MEG.bw$maternal_preference.W22 > 0.75,"RER>0.75","no.imprint"))
B73.only.MEG.bw$group <- "B73.MEG.BW"

W22.only.MEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bw.syntelogs.merge$filter.imprint.W22 == "MEG")
W22.only.MEG.bw$category <- ifelse(is.na(W22.only.MEG.bw$maternal_preference.B73),"low.coverage",
                                   ifelse(W22.only.MEG.bw$maternal_preference.B73 > 0.75,"RER>0.75","no.imprint"))
W22.only.MEG.bw$group <- "W22.MEG.BW"

B73.only.MEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "no.imprint")
B73.only.MEG.bp$category <- ifelse(is.na(B73.only.MEG.bp$maternal_preference.PH207),"low.coverage",
                                   ifelse(B73.only.MEG.bp$maternal_preference.PH207 > 0.75,"RER>0.75","no.imprint"))
B73.only.MEG.bp$group <- "B73.MEG.BP"

PH207.only.MEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "MEG")
PH207.only.MEG.bp$category <- ifelse(is.na(PH207.only.MEG.bp$maternal_preference.B73),"low.coverage",
                                   ifelse(PH207.only.MEG.bp$maternal_preference.B73 > 0.75,"RER>0.75","no.imprint"))
PH207.only.MEG.bp$group <- "PH207.MEG.BP"


not.enough.mat.bias <- sum(nrow(subset(B73.only.MEG.bw,B73.only.MEG.bw$maternal_preference.W22 > 0.75)),
                           nrow(subset(W22.only.MEG.bw,W22.only.MEG.bw$maternal_preference.B73 > 0.75)))
total.non.overlap <- sum(nrow(B73.only.MEG.bw),nrow(W22.only.MEG.bw))
not.enough.mat.bias / total.non.overlap * 100

not.enough.mat.data <- sum(nrow(subset(B73.only.MEG.bw,is.na(B73.only.MEG.bw$maternal_preference.W22))),
                           nrow(subset(W22.only.MEG.bw,is.na(W22.only.MEG.bw$maternal_preference.B73))))
not.enough.mat.data / total.non.overlap * 100

MEG.non.overlap <- rbind(B73.only.MEG.bw[,c("group","category")],W22.only.MEG.bw[,c("group","category")],B73.only.MEG.bp[,c("group","category")],PH207.only.MEG.bp[,c("group","category")])
plot.MEG.non.overlap <- data.frame(table(MEG.non.overlap))
plot.MEG.non.overlap$category <- factor(plot.MEG.non.overlap$category, levels=c("no.imprint","low.coverage","RER>0.75"))
plot.MEG.non.overlap$group <- factor(plot.MEG.non.overlap$group,levels=c("B73.MEG.BW","W22.MEG.BW","B73.MEG.BP","PH207.MEG.BP"))

ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.MEG.non.overlap,stat="identity",position=position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Reds")
```
What about PEGs? Use B73 by W22 contrast for numbers
```{r}
B73.only.PEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "no.imprint")
B73.only.PEG.bw$category <- ifelse(is.na(B73.only.PEG.bw$maternal_preference.W22),"low.coverage",
                                   ifelse(B73.only.PEG.bw$maternal_preference.W22 < 0.25,"RER<0.25","no.imprint"))
B73.only.PEG.bw$group <- "B73.PEG.BW"

W22.only.PEG.bw <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bw.syntelogs.merge$filter.imprint.W22 == "PEG")
W22.only.PEG.bw$category <- ifelse(is.na(W22.only.PEG.bw$maternal_preference.B73),"low.coverage",
                                   ifelse(W22.only.PEG.bw$maternal_preference.B73 < 0.25,"RER<0.25","no.imprint"))
W22.only.PEG.bw$group <- "W22.PEG.BW"

B73.only.PEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "no.imprint")
B73.only.PEG.bp$category <- ifelse(is.na(B73.only.PEG.bp$maternal_preference.PH207),"low.coverage",
                                   ifelse(B73.only.PEG.bp$maternal_preference.PH207 < 0.25,"RER<0.25","no.imprint"))
B73.only.PEG.bp$group <- "B73.PEG.BP"

PH207.only.PEG.bp <- subset(compare.bp.syntelogs.merge,compare.bp.syntelogs.merge$filter.imprint.B73 == "no.imprint" & compare.bp.syntelogs.merge$filter.imprint.PH207 == "PEG")
PH207.only.PEG.bp$category <- ifelse(is.na(PH207.only.PEG.bp$maternal_preference.B73),"low.coverage",
                                   ifelse(PH207.only.PEG.bp$maternal_preference.B73 < 0.25,"RER<0.25","no.imprint"))
PH207.only.PEG.bp$group <- "PH207.PEG.BP"


not.enough.pat.bias <- sum(nrow(subset(B73.only.PEG.bw,B73.only.PEG.bw$maternal_preference.W22 < 0.25)),
                           nrow(subset(W22.only.PEG.bw,W22.only.PEG.bw$maternal_preference.B73 > 0.25)))
total.non.overlap.pat <- sum(nrow(B73.only.PEG.bw),nrow(W22.only.PEG.bw))
not.enough.pat.bias / total.non.overlap.pat * 100

not.enough.pat.data <- sum(nrow(subset(B73.only.PEG.bw,is.na(B73.only.PEG.bw$maternal_preference.W22))),
                           nrow(subset(W22.only.PEG.bw,is.na(W22.only.PEG.bw$maternal_preference.B73))))
not.enough.pat.data / total.non.overlap.pat * 100

PEG.non.overlap <- rbind(B73.only.PEG.bw[,c("group","category")],W22.only.PEG.bw[,c("group","category")],B73.only.PEG.bp[,c("group","category")],PH207.only.PEG.bp[,c("group","category")])
plot.PEG.non.overlap <- data.frame(table(PEG.non.overlap))
plot.PEG.non.overlap$category <- factor(plot.PEG.non.overlap$category, levels=c("no.imprint","low.coverage","RER<0.25"))
plot.PEG.non.overlap$group <- factor(plot.PEG.non.overlap$group,levels=c("B73.PEG.BW","W22.PEG.BW","B73.PEG.BP","PH207.PEG.BP"))

ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.PEG.non.overlap,stat="identity",position=position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Blues")
```
Combined
```{r}
a <- ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.MEG.non.overlap,stat="identity",position=position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Reds")
b <- ggplot() + geom_bar(aes(x=group,y=Freq,fill=category),data=plot.PEG.non.overlap,stat="identity",position=position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette="Blues")

grid.arrange(a,b,nrow=1)
```




Create file of BW calls 
```{r}
tmp.meg <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "MEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "MEG")
tmp.peg <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 == "PEG" & compare.bw.syntelogs.merge$filter.imprint.W22 == "PEG")
tmp3 <- subset(compare.bw.syntelogs.merge,compare.bw.syntelogs.merge$filter.imprint.B73 != compare.bw.syntelogs.merge$filter.imprint.W22)

bw.out.share <- plot.out2.bw
bw.out.share$syntelog <- ifelse(bw.out.share$feature_type == "TE", "na.te",
                                ifelse(bw.out.share$feature %in% gene.key.BW.syntelogs$v4_gene_model
                                       | bw.out.share$feature %in% gene.key.BW.syntelogs$W22.Zm00004b.1., "syntelog","non.syntelog"))
bw.out.share$imprinted.both <- ifelse(bw.out.share$syntelog == "na.te" | bw.out.share$syntelog == "non.syntelog",bw.out.share$syntelog,
                                      ifelse(bw.out.share$feature %in% tmp.meg$feature | bw.out.share$feature %in% tmp.meg$W22.Zm00004b.1.,"both.megs",
                                      ifelse(bw.out.share$feature %in% tmp.peg$feature | bw.out.share$feature %in% tmp.peg$W22.Zm00004b.1.,"both.pegs",
                                             ifelse(bw.out.share$feature %in% tmp3$feature | bw.out.share$feature %in% tmp3$W22.Zm00004b.1.,"inconsistent.imprint","no.imprint"))))
```

```{r}
tmp1 <- bw.out.share[bw.out.share$genome == "B73",c("feature","filter.imprint","syntelog","imprinted.both")]
names(tmp1)[2:4] <- c("filter.imprint.BW","syntelog.BW","imprinted.both.BW")
tmp2 <- plot.out2.bp[plot.out2.bp$genome == "B73",c("feature","filter.imprint")]
names(tmp2)[2] <- "filter.imprint.BP"
tmp2$syntelog.BP <- ifelse(nchar(tmp2$feature) == 21, "na.te",
                                ifelse(tmp2$feature %in% gene.key.bp.syntelogs$v4_gene_model, "syntelog","non.syntelog"))
b.both.contrasts <- merge(tmp1,tmp2,by="feature",all=T)
b.both.contrasts$filter.imprint.BW[is.na(b.both.contrasts$filter.imprint.BW)] <- "not.detected"
b.both.contrasts$filter.imprint.BP[is.na(b.both.contrasts$filter.imprint.BP)] <- "not.detected"
b.both.contrasts[is.na(b.both.contrasts)] <- "NA"
b.both.contrasts$feature_type <- ifelse(nchar(b.both.contrasts$feature) == 21, "TE","gene")
b.both.contrasts$imprint <- ifelse(b.both.contrasts$feature_type == "TE" & (b.both.contrasts$filter.imprint.BP == "MEG" | b.both.contrasts$filter.imprint.BW == "MEG"),"matTE",
                ifelse(b.both.contrasts$filter.imprint.BP == "MEG" | b.both.contrasts$filter.imprint.BW == "MEG","MEG",
                                    ifelse(b.both.contrasts$feature_type == "gene" & (b.both.contrasts$filter.imprint.BP == "PEG" | b.both.contrasts$filter.imprint.BW == "PEG"),"PEG","no.imprint")))
b.both.contrasts$syntelog <- ifelse(b.both.contrasts$feature == "TE", "na.TE",
                                    ifelse(b.both.contrasts$feature %in% gene.key.BWP$v4_gene_model, "conserved.maize","variable"))
table(b.both.contrasts[,c("filter.imprint.BW","filter.imprint.BP")])
```
What proportion of the syntelog vs non-syntelogs agree across contrasts?
```{r}
b.both.contrasts$agreement <- ifelse(b.both.contrasts$filter.imprint.BP == b.both.contrasts$filter.imprint.BW, "matched.call","mismatched_call")
syntelog.agreement <- data.frame(b.both.contrasts %>% group_by(syntelog,agreement,imprint) %>% dplyr::summarise(features = n()))

ggplot() + geom_bar(aes(x=syntelog,y=features,fill=agreement),data=subset(syntelog.agreement,syntelog.agreement$imprint != "no.imprint"),stat="identity",position = position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + facet_grid(.~imprint,scales="free")
```



Compare to SNP-ASE method
```{r}
tmp <- subset(ase.reads2,ase.reads2$contrast == "BW" & ase.reads2$mean.count >= 10)
names(tmp) <- c("gid","ase.contrast","ase.BW.ratio","ase.BW.count")
bw.merge <- merge(plot.out2.bw,tmp,by.x="feature",by.y="gid",all.x=T)
tmp2 <- subset(ase.reads2,ase.reads2$contrast == "WB" & ase.reads2$mean.count >= 10)
names(tmp2) <- c("gid","ase.contrast","ase.WB.ratio","ase.WB.count")
bw.merge2 <- merge(bw.merge,tmp2,by.x="feature",by.y="gid",all.x=T)

bw.merge2$genotype.biased <- ifelse(bw.merge2$ase.BW.ratio >= 0.9 & bw.merge2$ase.WB.ratio <= 0.1, "B73.biased",ifelse(bw.merge2$ase.WB.ratio >= 0.9 & bw.merge2$ase.BW.ratio <= 0.1,"W22.biased","fine"))
bw.merge2$group <- factor(ifelse(bw.merge2$genotype.biased == "fine",bw.merge2$filter.imprint,bw.merge2$genotype.biased),levels = c("B73.biased","W22.biased","MEG","PEG","no.imprint"))

a <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.BW.ratio,y=maternal_preference)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE BxW") + ylab("RER") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")

b <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.WB.ratio,y=maternal_preference)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE WxB") + ylab("RER") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")

c <- ggExtra::ggMarginal(ggplot(bw.merge2,aes(x=ase.BW.ratio,y=ase.WB.ratio)) + theme_classic() + geom_point(aes(color = group,alpha = 0.5)) + xlab("SNP-ASE WxB") + ylab("SNP-ASE BxW") + theme(legend.position="none") + scale_color_manual(values = c("darkgreen","purple4","magenta","blue","gray")),type="histogram")

grid.arrange(a,b,c,nrow=2)
```



B73 vs W22
```{r}
a <- ggplot(bw.merge2,aes(x=ase.BW.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("SNP-ASE BxW") + ylab("RER")
b <- ggplot(bw.merge2,aes(x=ase.WB.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("SNP-ASE WxB") + ylab("RER")

grid.arrange(a,b,nrow=1)
```
Compare B73 vs PH207
```{r}
bp.merged.out <- merge(plot.out2.bp,subset(ase.reads2,ase.reads2$contrast == "BP"),by.x="feature",by.y="gid",all=F)
bp.merged.out2 <- subset(bp.merged.out,bp.merged.out$mean.count >= 10)
a <- ggplot(bp.merged.out2,aes(x=mean.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("Mean SNP Maternal Ratio BxW") + ylab("Maternal Preference Ratio")

bp.merged.out3 <- merge(plot.out2.bp,subset(ase.reads2,ase.reads2$contrast == "PB"),by.x="feature",by.y="gid",all=F)
bp.merged.out4 <- subset(bp.merged.out3,bp.merged.out3$mean.count >= 10)

b <- ggplot(bp.merged.out4,aes(x=mean.ratio,y=maternal_preference)) + geom_bin2d(bins = 50) + theme_classic() + scale_fill_viridis_c(option = "inferno") + xlab("Mean SNP Maternal Ratio WxB") + ylab("Maternal Preference Ratio")

grid.arrange(a,b,nrow=1)
```





Plot just genes
```{r}
plot.just.genes <- rbind(subset(plot.out2.bw,plot.out2.bw$feature_type == "gene"),subset(plot.out2.bp,plot.out2.bp$feature_type == "gene"),subset(plot.out2.wp,plot.out2.wp$feature_type == "gene"))

ggplot(plot.just.genes,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~contrast) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 

table(plot.just.genes[,c("genome","contrast","imprint")])
```

```{r}
meg.genes <- subset(b.both.contrasts,b.both.contrasts$imprint == "MEG")
length(meg.genes$feature[duplicated(meg.genes$feature)])
length(unique(meg.genes$feature))
```
```{r}
peg.genes <- subset(b.both.contrasts,b.both.contrasts$imprint == "PEG")
length(peg.genes$feature[duplicated(peg.genes$feature)])
length(unique(peg.genes$feature))
```
```{r}
meg.tes <- subset(b.both.contrasts,b.both.contrasts$imprint == "matTE")
length(meg.tes$feature[duplicated(meg.tes$feature)])
length(unique(meg.tes$feature))
```

Check endosperm vs pericarp expression, especially for MEGs
```{r}
unfiltered.meg.genes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "gene" & (b.both.contrasts$filter.imprint.BW == "MEG" | b.both.contrasts$filter.imprint.BW == "filtered.MEG"))

imprinted.meg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% unfiltered.meg.genes$feature & rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns])
pheatmap((imprinted.meg.4heat/rowMaxs(imprinted.meg.4heat)),border_color = NA,fontsize = 8,cluster_cols = F)
```
```{r}
a <- subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% rownames(peri.preferred))/rowMaxs(subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% rownames(peri.preferred)))
b <- subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% rownames(peri.preferred))/rowMaxs(subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% rownames(peri.preferred)))
imprinted.meg.4heat.v2 <- rbind(a[order(rowMeans(a),decreasing = T),],b[order(rowMeans(b),decreasing = T),])
pheatmap((imprinted.meg.4heat.v2/rowMaxs(imprinted.meg.4heat.v2)),border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,show_rownames = F,gaps_row = nrow(a))
```


Check for TEs
```{r}
peri.te.heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.tes$feature & rowMaxs(as.matrix(dev.rpm[,keep.columns])) > 0)[,keep.columns])
pheatmap((peri.te.heat/rowMaxs(peri.te.heat)),border_color = NA,fontsize = 8,cluster_cols = F)
```
```{r}
peri.te.heat.v2 <- peri.te.heat/rowMaxs(peri.te.heat)
pheatmap((peri.te.heat.v2[order(rowMeans(peri.te.heat.v2),decreasing = T),]),border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,show_rownames = F)
```

Plot just TEs
```{r}
plot.just.tes <- rbind(subset(plot.out2.bw,plot.out2.bw$feature_type == "TE"),subset(plot.out2.bp,plot.out2.bp$feature_type == "TE"),subset(plot.out2.wp,plot.out2.wp$feature_type == "TE"))

ggplot(plot.just.tes,aes(x=imprint,y=maternal_preference,fill=imprint)) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_grid(genome~contrast) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + stat_summary(fun.data = give.n, geom = "text") 

table(plot.just.tes[,c("genome","contrast","filter.imprint")])
```



Make distribution of all based on which libraries got included in DE calls
```{r}
imp.recips.rpm2 <- data.frame(rbind(subset(imp.BvW.rpm,imp.BvW.rpm$feature %in% rownames(imp.all.de.keep.bw)),subset(imp.BvP.rpm,imp.BvP.rpm$feature %in% rownames(imp.all.de.keep.bp)),subset(imp.WvP.rpm,imp.WvP.rpm$feature %in% rownames(imp.all.de.keep.wp))))

imp.recips.rpm2$A_mean <- rowMeans(imp.recips.rpm2[,1:3])
imp.recips.rpm2$B_mean <- rowMeans(imp.recips.rpm2[,4:6])
imp.recips.rpm2$maternal_preference <- ifelse(imp.recips.rpm2$type == "A",imp.recips.rpm2$A_mean/(imp.recips.rpm2$A_mean + imp.recips.rpm2$B_mean),imp.recips.rpm2$B_mean/(imp.recips.rpm2$A_mean + imp.recips.rpm2$B_mean)) # maternal preferance calculation

ggplot(imp.recips.rpm2,aes(x=genome,y=maternal_preference,fill=genome)) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_type~contrast,scales="free_x") + stat_summary(fun.data = give.n, geom = "text") 
```
Plot as stacked bars instead
```{r}
imp.recips.rpm2$mat.category <- factor(ifelse(imp.recips.rpm2$maternal_preference > 0.9, "strong.maternal",
                                       ifelse(imp.recips.rpm2$maternal_preference < 0.1, "strong.paternal",
                                              ifelse(imp.recips.rpm2$maternal_preference > 0.8, "moderate.maternal",
                                                     ifelse(imp.recips.rpm2$maternal_preference < 0.2, "moderate.paternal", "biparental")))),
                                      levels = c("strong.maternal","moderate.maternal","biparental","moderate.paternal","strong.paternal"))
imp.recips.rpm2$feature_category <- factor(ifelse(imp.recips.rpm2$feature_type == "TE","TE",
                                           ifelse(imp.recips.rpm2$feature %in% gene.key.BWP$v4_gene_model 
                                                  | imp.recips.rpm2$feature %in% gene.key.BWP$W22.Zm00004b.1. 
                                                  | imp.recips.rpm2$feature %in% gene.key.BWP$PH207.Zm00008a.1., "maize.syntenic.gene","maize.nonsyntenic.gene")),
                                           levels=c("maize.syntenic.gene","maize.nonsyntenic.gene","TE"))
stack.mat.category <- data.frame(imp.recips.rpm2 %>% group_by(feature_category,genome,contrast,mat.category) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(proportion = n/sum(n)))
stack.mat.category$sample <- paste(stack.mat.category$genome,stack.mat.category$contrast,sep="_")
ggplot() + geom_bar(aes(x=sample,y=proportion,fill=mat.category),data=stack.mat.category,stat="identity",position = position_fill()) + theme_classic() + scale_fill_manual(values=c("magenta","magenta4","darkgray","navy","blue")) + facet_wrap(.~feature_category,scales="free") + theme(axis.text.x = element_text(angle=90, hjust=1)) 
```
proportion.strong - syntenic genes
```{r}
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "maize.syntenic.gene" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)
```
TEs
```{r}
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "TE" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)
```
nonsyntenic genes
```{r}
tmp <- subset(stack.mat.category,stack.mat.category$feature_category == "maize.nonsyntenic.gene" & grepl("strong",stack.mat.category$mat.category))
tmp2 <- data.frame(tmp %>% group_by(sample) %>% dplyr::summarise(strong.parental = sum(proportion)))
mean(tmp2$strong.parental)
```

```{r}
ggplot(imp.recips.rpm2,aes(x=genome,y=maternal_preference,fill=genome)) + geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + facet_wrap(feature_category~contrast,scales="free_x") + stat_summary(fun.data = give.n, geom = "text") 
```


```{r}
gene.key.BWP$B.imp.BW <- ifelse(gene.key.BWP$v4_gene_model %in% subset(plot.out2.bw,plot.out2.bw$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$v4_gene_model %in% plot.out2.bw$feature,"not.imprinted","NA"))
gene.key.BWP$W.imp.BW <- ifelse(gene.key.BWP$W22.Zm00004b.1. %in% subset(plot.out2.bw,plot.out2.bw$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$W22.Zm00004b.1. %in% plot.out2.bw$feature,"not.imprinted","NA"))

gene.key.BWP$B.imp.BP <- ifelse(gene.key.BWP$v4_gene_model %in% subset(plot.out2.bp,plot.out2.bp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$v4_gene_model %in% plot.out2.bp$feature,"not.imprinted","NA"))
gene.key.BWP$P.imp.BP <- ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% subset(plot.out2.bp,plot.out2.bp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% plot.out2.bp$feature,"not.imprinted","NA"))

gene.key.BWP$P.imp.WP <- ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% subset(plot.out2.wp,plot.out2.wp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$PH207.Zm00008a.1. %in% plot.out2.wp$feature,"not.imprinted","NA"))
gene.key.BWP$W.imp.WP <- ifelse(gene.key.BWP$W22.Zm00004b.1. %in% subset(plot.out2.wp,plot.out2.wp$category != "notDE")[,"feature"],"imprinted",ifelse(gene.key.BWP$W22.Zm00004b.1. %in% plot.out2.wp$feature,"not.imprinted","NA"))
```

Look at endosperm-specific expression vs constituative expression
```{r}
endo.seed.columns <- c(names(dev.rpm)[grep("endosperm",names(dev.rpm))],names(dev.rpm)[grep("seed",names(dev.rpm))])
other.columns <- names(dev.rpm)[!names(dev.rpm) %in% endo.seed.columns]
other.columns <- other.columns[order(other.columns)]
endo.preferred <- rownames(subset(dev.rpm,rowSums(dev.rpm[,endo.seed.columns])/rowSums(dev.rpm) > 0.6))
plot.column.order <- c(endo.seed.columns,other.columns)
```

Summarize features further
```{r}
b.both.contrasts$expression.type <- ifelse(b.both.contrasts$feature %in% endo.preferred, "endo.preferred","constitutive")
```

MEG genes
```{r}
imprinted.meg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.genes$feature))

a <- subset(imprinted.meg.4heat,rownames(imprinted.meg.4heat) %in% endo.preferred)
b <- subset(imprinted.meg.4heat,!rownames(imprinted.meg.4heat) %in% endo.preferred)
c <- rbind(a,b)

pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of MEGs")
meg.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of MEGs")
```

PEG genes
```{r}
imprinted.peg.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% peg.genes$feature))

a <- subset(imprinted.peg.4heat,rownames(imprinted.peg.4heat) %in% endo.preferred)
b <- subset(imprinted.peg.4heat,!rownames(imprinted.peg.4heat) %in% endo.preferred)
c <- rbind(a,b)

pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of PEGs")
peg.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = F, main = "Developmental Expression of PEGs")
```
maternal TEs
```{r}
imprinted.te.4heat <- as.matrix(subset(dev.rpm, rownames(dev.rpm) %in% meg.tes$feature))

a <- subset(imprinted.te.4heat,rownames(imprinted.te.4heat) %in% endo.preferred)
b <- subset(imprinted.te.4heat,!rownames(imprinted.te.4heat) %in% endo.preferred)
c <- rbind(a,b)

pheatmap((a/rowMaxs(a))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((b/rowMaxs(b))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,clustering_distance_rows = "euclidean")
pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = T, main = "Developmental Expression of matTEs")
matTE.dev <- pheatmap((c/rowMaxs(c))[,plot.column.order],border_color = NA,fontsize = 8,cluster_cols = F,cluster_rows = F,gaps_col = length(endo.seed.columns),gaps_row = nrow(a),show_rownames = F,show_colnames = T, main = "Developmental Expression of matTEs")
```




Stacked bar with imprinted features, syntelog, expression.type
```{r}
of.interest <- subset(b.both.contrasts,b.both.contrasts$imprint != "no.imprint")
of.interest2 <- data.frame(of.interest %>% group_by(imprint,syntelog,expression.type) %>% dplyr::summarise(features = n()))
of.interest2$type <- factor(ifelse(of.interest2$imprint == "matTE", "matTE",paste(of.interest2$imprint,of.interest2$syntelog,sep="_")),
                            levels=c("PEG_conserved.maize","PEG_variable","MEG_conserved.maize","MEG_variable","matTE"))
of.interest2$expression.type <- factor(of.interest2$expression.type,levels=c("endo.preferred","constitutive"))

ggplot() + geom_bar(aes(x=type,y=features,fill=expression.type),data=of.interest2,stat="identity",position = position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_manual(values=c("darkorange","honeydew4")) 
```

Binding sites
Find binding sites from Batista et al. in B73
Convert sites to meme file
module load meme/4.12.0-py2-openmpi3-qbrm5zn
iupac2meme TTWCCATATW > batista_motif1_meme.txt
iupac2meme CCAWAAATGG > batista_motif2_meme.txt
cat batista_motif1_meme.txt batista_motif2_meme.txt > batista_motifs_meme.txt
module load bedtools2/2.27.1-s2mtpsu
cut -f 1,4,5,9 B73.structuralTEv2.2018.12.20.filteredTE.disjoined.gff3 | bedtools getfasta -name -fi Zea_mays.AGPv4.dna.toplevel.fa -bed - -fo B73v4.disjoinedTE.fa
fimo batista_motifs_meme.txt B73v4.disjoinedTE.fa > fimo.txt
cp fimo_out/fimo.txt batista_motifs_fimo_out.txt

grep gene Zea_mays.AGPv4.33.gff3 | cut -f 1,4,5,9 - | bedtools getfasta -name -fi Zea_mays.AGPv4.dna.toplevel.fa -bed - -fo B73v4.gene.fa
fimo batista_motifs_meme.txt B73v4.gene.fa 
cp fimo_out/fimo.txt batista_motifs_fimo_gene_out.txt

```{r}
all.sites <- read.table("batista_motifs_fimo_out.txt",header=F,stringsAsFactors = F,sep="\t")
all.sites$te <- substr(all.sites$V3,4,24)
sites.per.te <- data.frame(all.sites %>% group_by(te) %>% dplyr::summarise(motif1 = sum(V1 == "TTWCCATATW"),motif2 = sum(V1 == "CCAWAAATGG")))
sites.per.te$motif.type <- ifelse(sites.per.te$motif1 > 0 & sites.per.te$motif2 > 0, "both",
                                  ifelse(sites.per.te$motif1 > 0, "motif1","motif2"))
names(sites.per.te)[1] <- "feature"
sites.per.te$feature_type <- "TE"
```

Gene binding sites
```{r}
gene.sites <- read.table("batista_motifs_fimo_gene_out.txt",header=F,stringsAsFactors = F,sep="\t")
gene.sites$gene <- substr(gene.sites$V3,4,17)
gene.sites2 <- subset(gene.sites,!grepl("GRMZM",gene.sites$gene))
sites.per.gene <- data.frame(gene.sites2 %>% group_by(gene) %>% dplyr::summarise(motif1 = sum(V1 == "TTWCCATATW"),motif2 = sum(V1 == "CCAWAAATGG")))
sites.per.gene$motif.type <- ifelse(sites.per.gene$motif1 > 0 & sites.per.gene$motif2 > 0, "both",
                                  ifelse(sites.per.gene$motif1 > 0, "motif1","motif2"))
names(sites.per.gene)[1] <- "feature"
sites.per.gene$feature_type <- "gene"
```

```{r}
sites.per.feature <- data.frame(rbind(sites.per.gene,sites.per.te))
#write.table(sites.per.feature,file="PHE1_binding_sites_genesTEs.txt",quote=F,sep="\t",row.names = F)
```

```{r}
sites.per.feature <- read.table("PHE1_binding_sites_genesTEs.txt",sep = "\t",stringsAsFactors = F,header=T)
```


```{r}
b.both.contrasts$motif1 <- b.both.contrasts$feature %in% subset(sites.per.feature,sites.per.feature$motif1 > 0)[,"feature"]
b.both.contrasts$motif2 <- b.both.contrasts$feature %in% subset(sites.per.feature,sites.per.feature$motif2 > 0)[,"feature"]
b.both.contrasts$motif.type <- ifelse(b.both.contrasts$motif1 & b.both.contrasts$motif2, "both",
                                      ifelse(b.both.contrasts$motif1, "motif1",ifelse(b.both.contrasts$motif2,"motif2","no.motif")))
table(b.both.contrasts$motif.type)
```

```{r}
sites.summary <- data.frame(b.both.contrasts %>% group_by(imprint,motif.type) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))

ggplot() + geom_bar(aes(x=imprint,y=n,fill=motif.type),data=sites.summary,stat="identity",position = position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) +  scale_fill_brewer(palette = "YlGnBu")
ggplot() + geom_bar(aes(x=imprint,y=freq,fill=motif.type),data=sites.summary,stat="identity",position = position_dodge()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) +  scale_fill_brewer(palette = "YlGnBu")

b.both.contrasts$either.motif <- ifelse(b.both.contrasts$motif.type == "no.motif","no.motif","has.motif")
sites.summary2 <- data.frame(b.both.contrasts %>% group_by(imprint,either.motif) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))
sites.summary2
```

Look at per TE fam 
```{r}
te.both.contrasts <- subset(b.both.contrasts,b.both.contrasts$imprint == "matTE")
te.both.contrasts$fam <- substr(te.both.contrasts$feature,0,8)

tmp <- data.frame(table(te.both.contrasts$fam))
tmp2 <- subset(tmp,tmp$Freq>1)

site.te.summary <- data.frame(subset(te.both.contrasts,te.both.contrasts$fam %in% tmp2$Var1) %>% group_by(motif.type,fam) %>% dplyr::summarise(features = n()))

ggplot() + geom_bar(aes(x=fam,y=features,fill=motif.type),data=site.te.summary,stat="identity",position = position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) 
```
DHH2
```{r}
DHH2.both.contrasts <- subset(b.both.contrasts,grepl("DHH00002",b.both.contrasts$feature))

DHH2.not.expressed <- subset(imp.all,grepl("DHH00002Zm00001d",imp.all$ID) & !imp.all$ID %in% b.both.contrasts$feature)
DHH2.not.expressed2 <- data.frame(matrix(ncol=ncol(DHH2.both.contrasts),nrow=nrow(DHH2.not.expressed)))
colnames(DHH2.not.expressed2) <- colnames(DHH2.both.contrasts)
DHH2.not.expressed2$feature <- DHH2.not.expressed$ID
DHH2.not.expressed2$imprint <- "not.detected"

DHH2.both.contrasts2 <- rbind(DHH2.both.contrasts,DHH2.not.expressed2)

DHH2.both.sites <- merge(DHH2.both.contrasts2,sites.per.feature,by="feature",all.x=T)
names(DHH2.both.sites) <- gsub(".y",".sites",names(DHH2.both.sites),fixed = T)

DHH2.both.sites$motif1.sites[is.na(DHH2.both.sites$motif1.sites)] <- 0
DHH2.both.sites$motif2.sites[is.na(DHH2.both.sites$motif2.sites)] <- 0
DHH2.both.sites$motif.type <- ifelse(DHH2.both.sites$motif1.sites > 0 & DHH2.both.sites$motif2.sites > 0, "both",
                                      ifelse(DHH2.both.sites$motif1.sites > 0, "motif1",ifelse(DHH2.both.sites$motif2.sites > 0,"motif2","no.motif")))

site.dhh.summary <- data.frame(DHH2.both.sites %>% group_by(motif.type,imprint) %>% dplyr::summarise(features = n()))

ggplot() + geom_bar(aes(x=imprint,y=features,fill=motif.type),data=site.dhh.summary,stat="identity",position = position_fill()) + theme_classic()  +  theme(axis.text.x = element_text(angle=90, hjust=1)) + scale_fill_brewer(palette = "YlGnBu")

ggplot(DHH2.both.sites,aes(x=imprint,y=motif1.sites,fill=imprint)) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") 
ggplot(DHH2.both.sites,aes(x=imprint,y=motif2.sites,fill=imprint)) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank()) + stat_summary(fun.data = give.n, geom = "text") 
```




```{r}
b.both.contrasts$imprint.syntelog <- paste(b.both.contrasts$imprint,b.both.contrasts$syntelog,sep="_")
gene.key.syntenic.grasses <- subset(gene.key,nchar(gene.key$sorghum_ortholog) > 0 & nchar(gene.key$foxtail_millet_ortholog) > 0 & nchar(gene.key$rice_ortholog) > 0 & nchar(gene.key$brachypodium_ortholog) > 0)
b.both.contrasts$syntenic.grasses <- ifelse(b.both.contrasts$feature_type == "TE","NA.TE",ifelse(b.both.contrasts$feature %in% gene.key.syntenic.grasses$v4_gene_model, "syntenic.grasses","nonsyntenic.grasses"))
table(b.both.contrasts[,c("imprint","syntenic.grasses","syntelog")])

summarize.synteny1 <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint,syntelog) %>% dplyr::summarize(n=n()) %>% dplyr::mutate(freq=n/sum(n)))

a <- ggplot() + geom_bar(aes(x=imprint,y=freq,fill=syntelog),data=subset(summarize.synteny1,summarize.synteny1$syntelog == "PAV"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("green")) +  theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Nonsyntenic within maize")+ theme(legend.position = "none") + ylim(c(0,1))

summarize.synteny2 <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarize(n=n()) %>% dplyr::mutate(freq=n/sum(n)))

b <- ggplot() + geom_bar(aes(x=imprint,y=freq,fill=syntenic.grasses),data=subset(summarize.synteny2,summarize.synteny2$syntenic.grasses == "nonsyntenic.grasses"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("forestgreen")) +  theme(axis.text.x = element_text(angle=90, hjust=1))  + ggtitle("Nonsyntenic within grasses")+ theme(legend.position = "none") + ylim(c(0,1))

grid.arrange(a,b,nrow=1)
```

How many imprinted genes have GO terms?
File downloaded from AGRIGO using link: http://systemsbiology.cau.edu.cn/agriGOv2/download/958_slimGO on 20 Feb 2020
```{r}
gene.go <- read.table("B73v4_slimGO.txt",header=F,sep="\t",stringsAsFactors = F)
gene.go <- gene.go[,3:4]
names(gene.go) <- c("gene","term")
```

```{r}
b.both.contrasts$has.go.term <- factor(ifelse(b.both.contrasts$feature_type == "TE", "NA.TE",
                                       ifelse(b.both.contrasts$feature %in% gene.go$gene, "GO.term(s)","no.GO")),levels=c("GO.term(s)","no.GO"))
summarize.go <- data.frame(subset(b.both.contrasts,b.both.contrasts$feature_type == "gene") %>% group_by(imprint.syntelog,has.go.term,) %>% dplyr::summarise(n = n()) %>% dplyr::mutate(freq = n/sum(n)))

ggplot() + geom_bar(aes(x=imprint.syntelog,y=freq,fill=has.go.term),data=subset(summarize.go,summarize.go$has.go.term == "no.GO"),stat="identity",position = position_dodge()) + theme_classic() + scale_fill_manual(values = c("purple")) +  theme(axis.text.x = element_text(angle=90, hjust=1)) + ggtitle("Genes without GO term")+ theme(legend.position = "none") 
```

```{r}
b.both.contrasts.genes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "gene")
b.both.alluv <- data.frame(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint != "no.imprint") %>% group_by(imprint,syntelog,syntenic.grasses,has.go.term) %>% dplyr::summarise(n=n()))
ggplot(data=b.both.alluv,
       aes(axis4 = imprint,axis3=syntelog,axis2=syntenic.grasses,axis1=has.go.term,y=n)) + 
  scale_x_discrete(limits = rev(c("imprint","syntelog", "syntenic.grasses", "has.go.term")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = imprint)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
  theme_classic() + scale_fill_manual(values=c("magenta3","navy")) 
```

```{r}
b.both.alluv2 <- data.frame(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint != "no.imprint") %>% group_by(imprint,syntelog,has.go.term) %>% dplyr::summarise(n=n()))
ggplot(data=b.both.alluv2,
       aes(axis3 = imprint,axis2=syntelog,axis1=has.go.term,y=n)) +
  scale_x_discrete(limits = rev(c("imprint","syntelog", "has.go.term")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = imprint)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
  theme_classic() + scale_fill_manual(values=c("magenta3","navy")) 

```

```{r}
b.both.alluv2b <- data.frame(b.both.contrasts.genes %>% group_by(imprint,syntenic.grasses,has.go.term,expression.type) %>% dplyr::summarise(n=n()))
b.both.alluv2b$syntenic.grasses <- factor(b.both.alluv2b$syntenic.grasses,levels=c("nonsyntenic.grasses","syntenic.grasses"))
b.both.alluv2b$has.go.term <- factor(b.both.alluv2b$has.go.term,levels=c("no.GO","GO.term(s)"))
b.both.alluv2b$expression.type <- factor(b.both.alluv2b$expression.type,levels=c("endo.preferred","constitutive"))

ggplot(data=b.both.alluv2b,
       aes(axis4 = imprint,axis3=syntenic.grasses,axis2=has.go.term,axis1=expression.type,y=n)) +
  scale_x_discrete(limits = rev(c("imprint","syntenic.grasses", "has.go.term","expression.type")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = syntenic.grasses)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
  theme_classic() + scale_fill_manual(values=c("green","steelblue")) + facet_grid(.~imprint,scales="free")
```
Stats for above -  chisquared test
```{r}
m.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("MEG","no.imprint"))
a <- data.frame(m.stats %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntenic.grasses, values_from = n))
a$percent <- a$syntenic.grasses/rowSums(a[,2:3])
chisq.test(a[,2:3])

b <- data.frame(m.stats %>% group_by(imprint,has.go.term) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = has.go.term, values_from = n))
b$percent <- b$no.GO/rowSums(b[,2:3])
chisq.test(b[,2:3])

c <- data.frame(m.stats %>% group_by(imprint,expression.type) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = expression.type, values_from = n))
c$percent <- c$endo.preferred/rowSums(c[,2:3])
chisq.test(c[,2:3])

p.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("PEG","no.imprint"))
d <- data.frame(p.stats %>% group_by(imprint,syntenic.grasses) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntenic.grasses, values_from = n))
d$percent <- d$syntenic.grasses/rowSums(d[,2:3])
chisq.test(d[,2:3])

e <- data.frame(p.stats %>% group_by(imprint,has.go.term) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = has.go.term, values_from = n))
e$percent <- e$no.GO/rowSums(e[,2:3])
chisq.test(e[,2:3])

f <- data.frame(p.stats %>% group_by(imprint,expression.type) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = expression.type, values_from = n))
f$percent <- f$endo.preferred/rowSums(f[,2:3])
chisq.test(f[,2:3])
```
Version 2
```{r}
b.both.alluv2b <- data.frame(b.both.contrasts.genes %>% group_by(imprint,syntelog,syntenic.grasses,expression.type) %>% dplyr::summarise(n=n()))
b.both.alluv2b$syntenic.grasses <- factor(b.both.alluv2b$syntenic.grasses,levels=c("nonsyntenic.grasses","syntenic.grasses"))
b.both.alluv2b$syntelog <- factor(b.both.alluv2b$syntelog,levels=c("variable","conserved.maize"))
b.both.alluv2b$expression.type <- factor(b.both.alluv2b$expression.type,levels=c("endo.preferred","constitutive"))

ggplot(data=b.both.alluv2b,
       aes(axis4 = imprint,axis3 = syntelog,axis2=syntenic.grasses,axis1=expression.type,y=n)) +
  scale_x_discrete(limits = rev(c("imprint","syntelog","syntenic.grasses","expression.type")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = syntelog)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
  theme_classic() + scale_fill_manual(values=c("green","steelblue")) + facet_grid(.~imprint,scales="free")
```

```{r}
m.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("MEG","no.imprint"))
p.stats <- subset(b.both.alluv2b,b.both.alluv2b$imprint %in% c("PEG","no.imprint"))

a <- data.frame(m.stats %>% group_by(imprint,syntelog) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntelog, values_from = n))
chisq.test(a[,2:3])

d <- data.frame(p.stats %>% group_by(imprint,syntelog) %>% dplyr::summarise(n=sum(n)) %>% pivot_wider(names_from = syntelog, values_from = n))
chisq.test(d[,2:3])
```


```{r}
head(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "MEG" & b.both.contrasts.genes$syntenic.grasses == "nonsyntenic.grasses" & b.both.contrasts.genes$has.go.term == "no.GO"))
```








matTE things.
```{r}
b.both.te <- subset(b.both.contrasts,b.both.contrasts$feature_type == "TE")

shared.BWP <- subset(te.anno.compare, te.anno.compare$B73 == "present" & nchar(te.anno.compare$B73.annotation == 21) & te.anno.compare$W22 == "present" & nchar(te.anno.compare$W22.annotation == 21)& te.anno.compare$PH207 == "present" & nchar(te.anno.compare$PH207.annotation == 21))

b.both.te$shared.TE.BW <- ifelse(b.both.te$feature %in% shared.BW$B73.annotation, "shared.in.BW","variable.BW")
b.both.te$shared.TE.BP <- ifelse(b.both.te$feature %in% shared.bp$B73.annotation, "shared.in.BP","variable.BP")
b.both.te$shared.TE <- ifelse(b.both.te$feature %in% shared.BWP$B73.annotation, "shared.in.all","variable")

mat.tes <- subset(b.both.te,b.both.te$imprint == "matTE")
```

```{r}
mat.tes$order <- substr(mat.tes$feature,0,2)
table(mat.tes[,c("order","shared.TE")])
```

```{r}
mat.tes$fam <- substr(mat.tes$feature,0,8)
tmp <- data.frame(table(mat.tes$fam))
nrow(tmp)
subset(tmp,tmp$Freq > 1)
```

```{r}
b.both.tes <- subset(b.both.contrasts,b.both.contrasts$feature_type == "TE")
b.both.tes$order <- factor(substr(b.both.tes$feature,0,2),levels=c("DH","DT","RL","RI","RS"))
b.both.tes$shared.TE <- ifelse(b.both.tes$feature %in% shared.BWP$B73.annotation, "shared.in.all","variable")

mat.tes.alluv <- data.frame(subset(b.both.tes,b.both.tes$order %in% c("RL","DT","DH")) %>% group_by(imprint,order,shared.TE,expression.type) %>% dplyr::summarise(n = n()))

ggplot(data=mat.tes.alluv,
       aes(axis4 = imprint, axis3 = order,axis2=shared.TE,axis1=expression.type,y=n)) +
  scale_x_discrete(limits = rev(c("imprint","order","shared.TE", "expression.type")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = order)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) + coord_flip()+
  theme_classic() + facet_grid(.~imprint,scales="free") + scale_fill_manual(values=c("steelblue","green","darkorchid3"))
```

```{r}
mat.tes.alluv <- data.frame(b.both.tes %>% group_by(imprint,order,shared.TE,expression.type) %>% dplyr::summarise(n = n()))

ggplot(data=subset(mat.tes.alluv,mat.tes.alluv$imprint == "matTE"),
       aes(axis3 = order,axis2=shared.TE,axis1=expression.type,y=n)) +
  scale_x_discrete(limits = rev(c("order","shared.TE", "expression.type")), expand = c(.1, .05)) +
  xlab("features") +
  geom_alluvium(aes(fill = order)) +
  geom_stratum() + geom_text(stat = "stratum", infer.label = TRUE) +
  theme_classic() + scale_fill_manual(values=c("steelblue","green","darkorchid3","hotpink")) + coord_flip()
```



```{r}
B73.te.attributes <- read.table("B73_filteredTE_combined_attributes_12.20.18.txt",header=T,stringsAsFactors = F,sep="\t")
B73.te.attributes$imprint <- ifelse(B73.te.attributes$TE %in% mat.tes$feature, "matTE",
                          ifelse(B73.te.attributes$TE %in% b.both.contrasts$feature, "no.imprint","not.detected"))
LTR.age <- subset(B73.te.attributes,!is.na(B73.te.attributes$LTRsimilarity))
ggplot(LTR.age,aes(x=imprint,y=LTRsimilarity,fill=imprint))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_violin() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())   + stat_summary(fun.data = give.n, geom = "text") 
```

```{r}
ggplot(subset(B73.te.attributes,B73.te.attributes$order %in% c("RL","DT","DH")),aes(x=imprint,y=disjoined,fill=imprint))+ geom_abline(slope=0, intercept=.33) + geom_abline(slope=0, intercept=.66) + geom_boxplot() + theme_classic() + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())   + stat_summary(fun.data = give.n, geom = "text") + facet_wrap(.~order,scales = "free")
```

Tissue-specific expression
```{r}
table(b.both.te[,c("imprint","expression.type")])
133/(133 + 12)
1465/(1465 + 1247)
```


are maternal TEs enriched for their closest gene being a MEG?
```{r}
# write.table(meg.tes[,1,drop=F],file="matTEs.txt",quote=F,row.names = F,col.names = F)
```

# Find closest gene to matTEs
module load bedtools2/2.27.1-s2mtpsu
sort -k 1,1 -k4,4n matTEs.gff3 | bedtools closest -D b -t all -wa -wb  -a - -b /work/LAS/sna-lab/sna/B74v4/Zea_mays.AGPv4.33.gene.gff3 > matTE_closest_gene_v2.bed
cut -f 9,18,19 matTE_closest_gene_v2.bed > matTE_closest_gene_v2.txt

```{r}
matTE_closest_gene <- read.table("matTE_closest_gene_v2.txt",header=F,stringsAsFactors = F,sep="\t")
matTE_closest_gene$TE <- substr(matTE_closest_gene$V1,4,24)
matTE_closest_gene$gene <- substr(matTE_closest_gene$V2,4,17)
matTE_closest_gene$distance <- matTE_closest_gene$V3
matTE_closest_gene$TE.type <- ifelse(matTE_closest_gene$TE %in% meg.tes$feature,"matTE","No.Imprint")
matTE_closest_gene$gene.type <- ifelse(matTE_closest_gene$gene %in% meg.genes$feature,"MEG",
                                       ifelse(matTE_closest_gene$gene %in% peg.genes$feature, "PEG","No.Imprint"))
matTE_closest_gene$distance.cat <- ifelse(matTE_closest_gene$distance == 0, "overlap",
                                          ifelse(abs(matTE_closest_gene$distance) < 5000, "near","far"))
table(matTE_closest_gene[,c("gene.type","distance.cat")])
table(matTE_closest_gene[,c("gene.type","TE.type")])
```
Binomial test - are matTEs enriched for having MEG as nearest gene?
```{r}
successes <- nrow(subset(matTE_closest_gene,matTE_closest_gene$gene.type == "MEG"))
trials <- length(unique(matTE_closest_gene$TE))
proportion.genes.MEG <- nrow(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "MEG"))/nrow(b.both.contrasts.genes)
binom.test(successes,trials,proportion.genes.MEG,alternative = "greater")

successes.peg <- 0
proportion.genes.PEG <- nrow(subset(b.both.contrasts.genes,b.both.contrasts.genes$imprint == "PEG"))/nrow(b.both.contrasts.genes)
binom.test(successes.peg,trials,proportion.genes.PEG,alternative = "less")

plot.test <- data.frame(matrix(NA,nrow=4,ncol=3))
names(plot.test) <- c("set","imprint","freq")
plot.test$imprint <- c("MEG","MEG","PEG","PEG")
plot.test$set <- c("MEG.success","proportion.MEG","PEG.success","proportion.PEG")
plot.test$freq <- c(successes/trials,proportion.genes.MEG,0,proportion.genes.PEG)

ggplot() + geom_bar(aes(x=set,y=freq,fill="red"),data=plot.test,stat="identity",position = "identity", show.legend = FALSE) + theme_minimal() + ylim(0,0.15)  +  theme(axis.text.x = element_text(angle=90, hjust=1),panel.grid.major.x = element_blank()) + facet_wrap(imprint~.,scales="free_x")
```
Make version with Ns and where upstream and downstream are shown
```{r}
matTE.MEG <- subset(matTE_closest_gene,matTE_closest_gene$gene.type == "MEG")[,c("TE","gene","distance","TE.type","gene.type")]

plot.test2 <- plot.test[2:4,1:2]
plot.test2$count <- c(proportion.genes.MEG * trials,0,proportion.genes.PEG * trials)
plot.test2$group <- c("expected","PEG","expected")
plot.test2[4,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance < 0)),"upstream")
plot.test2[5,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance == 0)),"overlap")
plot.test2[6,] <- c("MEG.success","MEG",nrow(subset(matTE.MEG,matTE.MEG$distance > 0)),"downstream")
plot.test2$count <- as.numeric(plot.test2$count)
plot.test2$group <- factor(plot.test2$group,levels=c("upstream","overlap","downstream","expected","PEG"))
plot.test2$set <- factor(c("MEG.expected","PEG.nearest","PEG.expected","MEG.nearest","MEG.nearest","MEG.nearest"),levels=c("MEG.nearest","MEG.expected","PEG.nearest","PEG.expected"))

ggplot() + geom_bar(aes(x=set,y=count,fill=group),color="black",data=plot.test2,stat="identity",position = "identity") + theme_minimal()  +  theme(axis.text.x = element_text(angle=90, hjust=1),panel.grid.major.x = element_blank()) + facet_wrap(imprint~.,scales="free_x") + scale_fill_manual(values=c("indianred1","honeydew","lightblue3","black","yellow")) + scale_y_continuous(breaks=c(0,2,4,6,8,10),limits = c(0,11)) 
```



Table of matTE nearest gene is MEG
```{r}
matTE.MEG$order <- substr(matTE.MEG$TE,0,3)
for(i in 1:nrow(matTE.MEG)){
  if(matTE.MEG[i,"gene"] %in% plot.out2.bw$feature){
    matTE.MEG[i,"ratio.BW.gene"] <- subset(plot.out2.bw,plot.out2.bw$feature == matTE.MEG[i,"gene"])[,"maternal_preference"]
  }
  else(matTE.MEG[i,"ratio.BW.gene"] <- "NA")
  if(matTE.MEG[i,"gene"] %in% plot.out2.bp$feature){
    matTE.MEG[i,"ratio.BP.gene"] <- subset(plot.out2.bp,plot.out2.bp$feature == matTE.MEG[i,"gene"])[,"maternal_preference"]
  }
  else(matTE.MEG[i,"ratio.BP.gene"] <- "NA")
  if(matTE.MEG[i,"TE"] %in% plot.out2.bw$feature){
    matTE.MEG[i,"ratio.BW.TE"] <- subset(plot.out2.bw,plot.out2.bw$feature == matTE.MEG[i,"TE"])[,"maternal_preference"]
  }
  else(matTE.MEG[i,"ratio.BW.TE"] <- "NA")
  if(matTE.MEG[i,"TE"] %in% plot.out2.bp$feature){
    matTE.MEG[i,"ratio.BP.TE"] <- subset(plot.out2.bp,plot.out2.bp$feature == matTE.MEG[i,"TE"])[,"maternal_preference"]
  }
  else(matTE.MEG[i,"ratio.BP.TE"] <- "NA")
  matTE.MEG[i,"gene.variability"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"gene"])[,"syntelog"]
  matTE.MEG[i,"gene.expression"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"gene"])[,"expression.type"]
  matTE.MEG[i,"TE.expression"] <- subset(b.both.contrasts,b.both.contrasts$feature == matTE.MEG[i,"TE"])[,"expression.type"]
}
#write.table(matTE.MEG[order(matTE.MEG$distance),],file="matTE_closest_gene_is_MEG_v2.txt",quote=F,sep="\t",row.names = F)
```

```{r}
matTE.MEG2 <- merge(matTE.MEG,te.anno.compare,by="TE",all.x=T)
```

Output files
```{r}
# write.table(b.both.contrasts,file="B73_features_both_contrasts.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.bw)[14] <- "RER"
# plot.out2.bw$imprint <- NULL
# write.table(plot.out2.bw,file="B73.v.W22_features.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.bp)[14] <- "RER"
# plot.out2.bp$imprint <- NULL
# write.table(plot.out2.bp,file="B73.v.PH207_features.out.txt",sep="\t",quote=F,row.names = F)
# names(plot.out2.wp)[14] <- "RER"
# plot.out2.wp$imprint <- NULL
# write.table(plot.out2.wp,file="W22.v.PH207_features.out.txt",sep="\t",quote=F,row.names = F)
```












